#Unsupervised Learning Market

[ict-123]

Unsupervised learning is a branch of artificial intelligence that involves the training of an algorithm on unstructured data. Unstructured data is defined as data that does not have any predefined categorizations or labels. 

[qwikskills]

Unleashing the Power of Machine Learning in the 21st Century

Machine learning is one of the most talked about and rapidly growing fields in the tech industry. It is a branch of artificial intelligence that allows computers to learn and make predictions or decisions without explicit programming. The rise of big data and the increasing availability of computing power have made it possible for machine learning algorithms to handle vast amounts of data and provide valuable insights and predictions.

In recent years, machine learning has been applied in various industries, ranging from healthcare to finance, retail, and marketing. In healthcare, machine learning algorithms are used to analyze patient data and help doctors make more accurate diagnoses. In finance, machine learning is used to detect fraud, analyze financial markets, and make investment decisions. In retail, machine learning is used to personalize shopping experiences, recommend products, and optimize pricing.

One of the key benefits of machine learning is that it allows for automated decision-making, which can save time and resources. Machine learning algorithms can analyze large amounts of data and provide insights in real-time, enabling organizations to make data-driven decisions more efficiently. Additionally, machine learning algorithms are able to improve over time, becoming more accurate as they are exposed to more data.

Despite its many advantages, machine learning is not without its challenges. One of the main challenges is the lack of transparency in decision-making. It can be difficult to understand how machine learning algorithms arrived at a particular decision, making it difficult to explain the decision to stakeholders. Additionally, machine learning algorithms can be biased if the data used to train them is biased, leading to unfair or inaccurate decisions.

In conclusion, machine learning is a powerful tool that has the potential to transform the way we live and work. As the technology continues to evolve and improve, we can expect to see more and more applications of machine learning in various industries. However, it is important to approach machine learning with caution and ensure that the algorithms are developed and used in a transparent and ethical manner.

[sunny374940]

I will teach you all I know

So here's a little story about Emmrich learning that his daughter is a mage.

Here it is on ao3

“FIRE!” Manfred was yelling excitedly from the living room. While this would not be an unusual occurrence in itself, the sound that followed made Emmrich's blood freeze. Ellie was screaming in fear and Emmrich was out of his study and down the stairs in mere seconds, skidding to a stop in front of his daughter. She was standing there, crying, and he fell down on his knees to examine her for any signs of injury, but blessedly there was nothing to be seen. So what was-

Ah. There was a burnt patch on the rug, Manfred must have stamped the fire out.

“Manfred?” he half asked, half admonished. He had directly forbidden any unsupervised magic inside the house and yet here they were.

“NOT MANFRED,” came the answer. Well, that mystery could wait, Emmrich decided, directing his attention back to Ellie. She was shaking, tears were still streaming down her face and she looked so terrified that Emmrich's heart almost broke at the sight. He drew her into an embrace, holding her tightly and stroking her hair until the tremors finally stopped. She squirmed in his arms and he let her go.

“Elanora, what happened? Are you alright?”

“It was an accident!” she wailed, on the verge of more tears. “I don't know what happened, it just started burning!” And then she was sobbing again and Emmrich picked her up to comfort her. She rested her head on his shoulder and his mind went back to the time when she was a baby, when he and Rook carried her through countless nights just like this. And now she was six years old and such a happy and clever child. Though not so happy at the moment, it seemed.

Emmrich looked at Manfred with a raised eyebrow.

“Manfred, could you please tell me what happened here?”

“SHE'S LIKE MANFRED! LIKE YOU!”

What? Like him? Surely not… “What do you mean?”

“MAGIC!” Manfred was clapping his hands in glee.

Emmrich gaped at him. She was a mage? She was a mage! Oh, but how he looked forward to teaching her! But that was for later, now he had a scared little girl in his arms who deserved an explanation.

“Ellie, would you come down for a bit?”

She sniffled but nodded against his shoulder and he settled her on the sofa and sat down next to her. He handed her a handkerchief and she blew her nose with a honk, giggling at the sound she produced. He rolled his eyes fondly. All was right in the world when his daughter was amused by bodily functions.

“Do you understand what happened?” he asked softly.

“Noo? But I didn't want to do it, I was playing dragons with Freddy and I was burning down a village and it was just pretend, but then the rug caught fire!” She was getting agitated again and Emmrich ran a hand down her back to help her calm down.

“I am not angry with you, my dear. You are showing the first signs of being able to use magic, though you are not yet able to control it.”

Her eyes lit up. “I'm gonna be a mage? Like you?”

“Yes, you are. But there is much studying ahead of you, if you wish to master it.”

She nodded seriously. “I can do it, daddy, don't worry.”

“Of that I have no doubt.”

Right then the front door opened, heralding Rook's return from the market. Ellie took off after him immediately.

“Dad, dad!” she yelled, running up to him and he caught her in a hug.

“Hey, little bug, how was your day?“

“I burned a hole in the rug and I'm gonna be a mage like daddy!” she said proudly, pointing at the rug in question. They walked back to Emmrich and Ellie plopped herself on the sofa, examining her hands hopefully for signs of more fire coming out of them.

“Oh. Okay?” Rook looked at Emmrich, more than a little confused.

“Rook, it is marvelous! She managed to produce flame all by herself. And at such a young age!”

“Wow, that's great! Well, not the rug, I mean. And she's gonna have the best teacher right at home.”

Rook said the last part with such conviction that Emmrich blushed a little at the words. Then Rook turned back to Ellie.

“Are you gonna be a necromancer like your daddy?”

“Nope. I wanna be cool like Neve and make ice knives!” She was animated now, making woosh-woosh noises, pretending to cast magic at an invisible opponent.

Emmrich sputtered at her comment and Rook's shoulders were shaking with quiet laughter.

“Excuse me, young lady, is commanding the dead not ‘cool’ enough for you?” he asked, pretending to be offended.She patted his hand consolingly.

“Neve can teach you to make ice too, if you want to be cool, you know.”

Rook was still snickering, but Emmrich pretended not to see.

“I shall remember that, then, should I ever wish to become cool like Neve and yourself,” he said haughtily, but there was laughter in his voice he couldn't quite hide.

He raised his gaze back to Rook, who was now watching him with a warm smile.

“So there are gonna be three mages in the house now? I'm beginning to feel a little outnumbered,” Rook chuckled, taking a hold of Emmrich's hand. “But I guess I'll manage, they're family, after all.”

Emmrich smiled back at him, still as hopelessly in love with his husband as he was when they first got together. Rook tugged at his hand to bring him closer and then he kissed him in belated greeting. Emmrich reciprocated happily, until-

“Daaaad!”

Rook drew away and looked at their daughter with a grin.

“Yeah, El?”

“Ew. Take yourselves elsewhere please,” she said in such a good imitation of Emmrich's voice that they both burst out laughing.

[artistic-pussy-power]

An Ode to the Dust and the Potholes of India 

Inside India’s most beautiful state, Kerala, is its most beautiful town: Kodanchery, my hometown of dusty streets and polluted corners. (I think if you told some Indians burning plastic is bad, they would throw their personalities into the fire just to be spiteful). 

Kodanchery is loud and, as with every town in India, filled with rickety old shops. There’s the fish shop where my brother would live if he could. I’m sure the owner would quite like him to because there’s nothing that lights up his face as much as hearing my brother’s foreign English inside his smelly shop. 

Then there’s the open market stalls my achachan limps along, browsing for fruits and vegetables. They’re gathering with fruit files, but no one seems to care. It’s nothing that can’t be fixed with a wash anyway.  

Today, my cousins are home for the weekend and working away in their father’s “teashop” I guess you could call it. The shop is small but about six tables are shoved in there anyway. Like all "teashops” in my little state, there are no windows or doors, just the open, inviting front and spluttering fans whirling away. As we sit, my aunt comes from the small kitchen with a smile. She has my brother’s egg puffs, my neyyappam and all our chai in hand already. The snacks are held in a heated shelf just steps away from us, but she plates them anyway.  

In another life, we would make the 30-minute trek home on foot and my achachan would spend the entire time talking about the importance of daily exercise. In this life, his grotesquely swollen leg will bid us to call our other uncle. My ‘papapa’ picks us up in the autorickshaw he’s had as long as I’ve known him and achachan gives the lecture as we bump along.  

A later day, I will drive along our neighbourhood with my dad, learning the frustrations of manual. We skitter along the asphalt road, avoiding the edges that lead to unsupervised pits (there are many of them) and turn left at the small “Cross N’ Church” (it sounds better in Malayalam). The forest of rubber trees that give our little neighbourhood its livelihood rise above us and drip with white. We go over roads that might make my friends in New Zealand faint at the sheer sight and laugh about the time we crossed the border between Kerala and Tamil Nadu––how the potholes and eroded asphalt disappeared in the blink of an eye. 

On the way back, I wave to the house on the hill where my Malayalam teacher lives. Her guard dogs bark at us, and in the evening, when I go over to learn my mother tongue, the aggression will startle me. In a couple of weeks, I will start to smile at their roar. Today, I think they were cute—very cute.m

[dearestones]

Muscle Memory (Azul Ashengrotto and Reader)

Warnings: Exploration of merfolk culture, elaboration on certain aspects of canon lore, etc. 

@lottieinlimbo Request: Hi Devin!!! Congratulations on the 750 followers :D!! Your writing is incredible, you have more than earned your success!!!! I’m here to steal the final request slot! There were a lot of cool prompts that would have been fascinating with multiple characters, it was hard to choose! But I’m thinking “why did you help me?” With Azul could be really fun, maybe something platonic? I’m really excited to see what you do with this, congrats again on 750 followers!!

.

.

.

Once upon a time, you desperately wanted to be a child of the sea. 

It was a stupid thing to want, you know that now that you’re older, but as a child of a fisherman and a marine biologist, you grew up immersed in the sea. Swimming came as naturally as breathing and there were days when you refused to get out of the water. Your mother would spend hours either cataloging and tending to the wildlife off the shore or lecturing students at your local university. Your father, on the other hand, would board a boat with a few other fishermen and cast his net along the side of their boats, eager to catch fresh fish for dinner and to sell at the local market. 

If it were not for the legs that burned under the unforgiving sun, you would have been one of the merfolk. You told your mother so, and she laughed as she hastily scribbled annotations on a particularly bright student’s thesis. It wasn’t a demeaning laugh, it was more like a sound that sought to appease a precocious child like yourself. 

“If you were from the sea,” your mother had crooned in your ear, “then I wouldn’t be able to do this, would I?” 

She would tuck you deep into her chest before blowing raspberries into your cheek and tousling your hair.

“If you were a mer,” your father had told you, “I would cast nets out everyday so I can take you back home with me.” 

He would then throw you into the middle of your bed before gathering the covers and pulling the corners together to make it seem as if you were caught in a net. 

You couldn’t be a child of the sea, and even your young, naive mind knew that, so you latched onto the next best thing. 

Talking about them. 

Both your mother and father, having spent most of their childhood, adolescence, and adulthood living in and around the sea, had tales to tell of meeting merfolk. People who lived on the mainland, you found, had very few encounters with the children of the sea. Sevens, they even said that it was actually more likely for them to meet one of the fair folk visiting from Briar Valley. Your parents and quite a number of the locals had often talked to the children of the sea who inhabited the waters a few miles from the coast, but some of the small fry would frequent the shallow waters, often risking getting beached on shore. 

One of your first few encounters with merfolk happened when you were only six years old. You had long since learned how to swim (you learned when you were only nine months and since then, you begged your father to take you out to the beach whenever possible), but you were never left unsupervised. On this particular day, both your parents were free from their duties and had set up a little picnic spot quite away from the tide.

You had recently eaten a snack and because your parents were superstitious, said that you couldn't go swimming until at least thirty minutes had passed. You were six and you had no true grasp of consequences outside of your parents sternly talking to you or taking away privileges as punishment. Because of that, you were toeing the line between the water and hopping in between the craggy rocks that littered the area. You had been forced to wear special swim shoes to keep your feet safe, but even with your short sighted nature, cuts on your feet and punishment from your parents didn't make the idea of disobedience all that appealing.

You skipped down the share, sometimes slipping and tripping into the water, but you always laughed to yourself and waved back at your parents every once in a while. They kept a wary eye on you, but continued to converse in that way only adults could understand and children didn't feel like grasping.

Eventually, you wandered far enough that you couldn't see or even hear your parents anymore. That may have frightened any other child, but you grew up with the sea practically in your backyard and you were taught how to self-rescue since before you could learn how to walk.

You were safe and to a certain extent, your parents trusted you.

As you waded in and out of the water that only reached your ankles, you caught sight of a boulder jutting out of the water. It was several feet away from you, away from the remnants from the stolid presence of the beach and trespassing into the heart of the sea. Normally, with how high the tide can get, the boulder would be submerged, but today, you could see how craggy and sharp it really looked.

So, it startled you when you saw a ruddy face jut out from the other side of the boulder, lock eyes with you for a second, before shrieking and backing away.

Friend?

Was it a friend?

At that moment, you weren't thinking about your parents, if enough time had passed, or if you would look stupid with your shoes still on. When you locked eyes with a child your own age, you could only hope that they would be your friend.

You ran into the water, quickly started swimming once you were deep enough, and swam towards the other side of the boulder.

Would the child still be there?

You swam onto the other side of the boulder until you saw the child, except there was—

"You have a tail!" You could barely hold onto your excitement. It was only due to societal convention that you didn't start tugging and prodding at the brilliant appendage that dangled and swished in front of you.

"And you have legs."

You looked up to find a face both like and unlike your own.

"And it's not just a tail! I've got eight of them!" The child splashed you with one powerful swish of her tails. “And they’re called arms!”

And that's how you met your first friend from the sea.

Shelley was a native from the sea bordering the Land of the Red Dragon. Her family used to live in those warm waters until they decided to migrate towards your stretch of ocean. You were young, so you didn't quite understand what she was talking about when she mentioned migratory patterns, politics, or whatever, but you did latch onto the fact that she was lonely and had a hard time making friends.

So you offered to be her friend.

"With a human?" Shelley scoffed.

Well, sixteen years later and she was still splashing you. Both of you grew up together, often sharing aspirations and dreams with each other. Like the mermaid princess, she dreamed of going up on land and walking on her own pair of legs. It took a while, but she managed to get into an exclusive land boot camp that occurred on another island (you pouted when she told you) and was later given a regular supply of transformation potions once she was old enough to enroll into university. 

And that’s when you realized that the both of you could still work together.

You see, over the years, you began to realize that your love of merfolk wasn’t just because you wanted to befriend them, but you also wanted to get to know their culture better. Like the fae, children of the sea were notorious for keeping close to themselves, especially those who spent their formative years in the bathypelagic zones. What most land dwellers knew about their aquatic neighbors were either from the outdated accounts from the famed mermaid princess or the very few merfolk who decided to breach the surface. Most governments wanted to bridge the gap between the vastly different cultures, which meant that they often recruited both merfolk and land dwellers to facilitate and improve upon the land boot camps that were initially started by the very first royal who deemed it worthy to live on land. 

You broached the topic cautiously to Shelley when you had been busy helping her walk across the beach after a session at her land boot camp. 

It took some convincing, but the both of you decided that you would work together: one day, you would be a physical therapist to help merfolk walk upon land and Shelley would teach classes to acclimate them to land culture. 

This year, you were supposed to help merfolk native to the Coral Sea stand and walk for the first time. 

This land boot camp you had relocated to was situated on one of the small islands under the Land of Dawning’s jurisdiction. The facility was small and private, owing to the fact that it was still a government project and no one wanted to be ogled at by civilians, especially when they were learning how to be human for the first time. 

This batch of merfolk were from the mesopelagic or twilight zone of the ocean, second to the sunny euphotic zone. You had your fair share of merfolk from across all three main zones, but most participants originated from the upper layer, owing to the fact that there was an increase in land dwellers to merfolk interactions. So, it wasn’t a surprise to find less than a dozen merfolk swimming playfully in the water or waiting patiently upon rocks. 

After taking the transformation potion (each of them tailored to the specific type of sea dweller because it would be a disaster and a half giving one of the classical looking mers the dosage that would better benefit a mer modeled after an angler fish or a cecaelia), all of them would be given the chance to stand up and walk within the water. 

Years ago, when the mermaid princess made her way ashore, it was only through willpower alone that she could heave herself off the sandy beach and trudge her way into her beloved’s castle. Today’s research declared that it was better to treat merfolk with hydrotherapy first by giving them exercises that would acclimate them to using the right muscles and granting them muscle memory that would later aid them in land. The water helped with the sudden pull of gravity while also maintaining a sense of familiarity so as to not cause them undue stress. 

(It was also for the same reason it was strictly forbidden that government workers such as your supervisor and by extension, yourself, were not allowed to teach the merfolk how to walk in a pool. The chlorine contact would not cause them undue harm since they now had human skin, but mers were notoriously sensitive to dryness and that wouldn’t exactly be the best impression to make when fostering better relations across races). 

The same was said for clothing. 

Get the merfolk accustomed to walking first and then clothes. 

It was best to get them hurtling one obstacle after another instead of tackling everything human-related at once. After at least one week of hydrotherapy and lessons on human etiquette, the second week would introduce clothing and walking on land. The third week would be full time human-ing, which meant a stronger dose of transformation potion that would last three days before a refill was needed. Finally, during the fourth week, they would be given the full transformation potion and a trip to the mainland where they would tour and interact with the locals as opposed to government agents. 

A month shouldn’t be enough time to get merfolk acquainted on land, but the newcomers from the twilight zone were hardy and could take a stronger beating than their neighbors in the higher zones. They already had strong muscles and coordination; they needed only guidance and direction on how to move those muscles properly in a new body. 

And so, after you reviewed your roster of attendees, you wore a wetsuit, slathered on sunscreen in copious amounts to stave off the harmful rays of the sun, and then ventured away from the facility on land. Upon first glance, you realized that most of them were young, but you saw that there were at least two middle aged adults and one elderly mer who congregated closer to the front of their younger comrades. Carefully, you studied them; how they moved, how their tails would ripple in the sunlight, and how some of them had teeth made for slicing. 

A mixed bag of both predator and prey. 

You would have to keep an eye out for any bullying. 

Once your supervisor was in position and began detailing the history of the government facility, you carefully tuned her out, content to dig out the tray full of transformation potions from the cooler that one of the other interns had brought out of the lab. Inside, vials of cerulean and silver gleamed within the confines of pale lavender vials. To each of the stoppers, a nametag and species of merfolk was affixed. Indeed, if you remembered correctly, the concentrations of cerulean and silver allowed a brief glimpse of what the potion could do: silver to bind appendages together, turn gills into scarred skin, transform beautiful scales and hides into varying shades of brown and tan; cerulean, to split tails in two, strengthen the bones, and allow for greater lung capacity. 

Depending on the type of mer, maybe they needed more of the components that made up cerulean to cut their legs into two. Or maybe they were a species of mer that took after squids or octopi; they needed more silver. 

All of the vials were encased in a thin layer of frost: optimal temperature for quick results. 

In another cooler, there were another set of vials. For these, all of the dosages were equal. 

Pain medication in case some of the merfolk would not be able to take the pain of sloughing away their old bodies for a new one. 

(It was rarer for the merfolk native to the deeper zones to ask for the pain medications, but you had seen it happen once or twice. Knowing them, however, and knowing that there were at least a ratio of three predators to one prey, it would be best for everyone involved that no one took the medication). 

And underneath the set of pain medication, there was also another tray filled with potions meant for emergencies, but you chose not to dwell on unpleasant possibilities. 

Finally, you heard your supervisor call for you and gesture for you to walk across the boardwalk and call out the names of the attendees. Like any roster, it was labeled in alphabetical order. A’s to B’s to C’s and so on. No surprise there. 

However, what caught your attention was that the first A on your roster (the only A, actually), belonged to a young male mer. He kept most of his body underwater with only the top of his head and eyes peeking out, but at your behest, he reluctantly began to climb out. It wasn’t until you caught sight of his multiple appendages attached to the bottom of his torso that you realized that you may know his type of species.

A cecaelia.

Out of habit, you counted all eight of his arms—exactly like Shelley!—but you must have been so entranced at the idea of working with another cecaelia because the boy made an irritated grunting noise as he narrowed his eyes at you. 

You apologized, slightly bemused by his impatience before giving him the vial that corresponded to his name. You murmured the instructions that he should wait until everyone else received their dose before ingesting it. At that, the cecaelia nodded, resolute and determined in his very reaction, before he ducked below the surface of the water and swam far away from the boardwalk. 

After that brief encounter, you continued down the list of names and made sure not to spend too much time mentally cataloging the types of merfolk who were supposed to be your students. There were at least two “classical” merfolk (very human at the torso with a nondescript, but shimmering tail at the bottom), but the rest were clearly descended from various species. You could see an angler fish, a squid, and even twin eels!

Twins!

If you remembered correctly, twins were very rare within the ocean, especially the deeper you went. The fact that there were two adolescent mers who were born at the same time and still lived despite the cruel nature of the ocean, could only spell good luck. Goodness, it didn’t even matter if they were predatory merfolk! You were sure they would be a delight to have under your tutelage. 

Finally, after each of the merfolk had received their government mandated potions, your supervisor called you and the other intern to stand in five feet intervals along the boardwalk. You were at the very end of the boardwalk and you were assigned to the first four merfolk along your roster. It was, of course, the cecaelia, but there were also two classical merfolk and an angler fish. The classical merfolk huddled together, giggling as they asked you questions about how long it normally took humans to learn how to walk. Meanwhile, the two other deep sea merfolk were content to keep to themselves, but you noted that the cecaelia was hanging onto your every word. 

Now that everyone was in position, your supervisor announced that in each group, only one mer would go at a time. This was to prevent them from drowning all at the same time. 

“Okay then!” You clapped your hands to gain all of your students’ attention. “Does anyone want to volunteer? Or would you rather go by alphabetical order?”

During your time as an intern, you were more accustomed to the merfolk who hailed from the euphotic zone. More often than not, the attendees were classical mers who were already aware of land dweller cultures and it showed—they were open and friendly, often clamoring and climbing over each other to be the first. For this particular group, you were disappointed, but not at all surprised to see that your chattier students had instantly quieted and looked to each other in apprehension. Meanwhile, the cecaelia and the angler fish mers looked like they were planning on completely submerging themselves. 

You took a deep breath before coming to a decision. 

“Mr. Azul… Ashengrotto?” You nodded at the cecaelia, watching as his face seemed to grow from a light lavender gray to a light flushed blue. Nodding at him, you sat upon the boardwalk and beckoned him forward with a kind smile and a wave. “Come here. For safety purposes, I’ll ask that after you drink the transformation potion, you hold onto me or the boardwalk. It will be painful, but the shock will pass once you start kicking your legs. Do you understand?”

The cecaelia, still having yet to speak in your presence, nodded his head. 

“Do you remember the demonstration on how to tread water?”

The cecaelia furrowed his brows, perhaps remembering how both your supervisor and fellow intern had jumped into the water earlier that day. They had hung onto the wood of the boardwalk while also slightly kicking underwater to keep themselves upright. Then, they gestured for you to sit to also demonstrate for some of the more nervous mers how to hold onto your arms or legs if they needed someone to further stabilize their movement and offer comfort.

With a decisive nod, he said, "Yes, I do. May I start?"

You said a few more things that were included in protocol just so the entire group under your watch understood the consequences for not taking the exercises seriously, to comply with legal regulations, and to allow each of the mers time to relax and understand that you knew what you were doing. Finally, after about five more minutes, you nodded to the cecaelia.

In your experience, taking any sort of potion was best done by downing the entire thing. It was recommended that the merfolk don't take pauses or take small sips. Transformation was already a tricky process, it would become all the more complicated if you delivered only a small dose of the potion to your body only to take a break for too long and have certain body parts rearranging themselves without the proper dosage. Thankfully, the young mer looked to be the studious type, and above all else, cautious, because once he unstoppered the vial, he returned the seal to you and knocked back the potion as if he were thirsty.

Within seconds, his face contorted in pain and his body began to thrash as his skin began receding from the shimmering lilac and gray undertones to an almost sickly pale coloring. You had assigned spotters earlier in case the mer undertaking the potion were too wracked with pain to come to their senses, so that meant that the angler fish began to restrain the cecaelia while your other students brought both of his hands up to the boardwalk. Under your keen eye, you could see that the skin above his knuckles were bone white and that face was flushing dark blue. You took note of that: scientists were always trying to improve upon the efficacy of their potions, cosmetics being one of the criteria that was often underrepresented.

"Breathe." You kept your voice steady. "Like the breathing exercises," you reminded him. "In through your nose and out through your mouth. Slow and steady."

His thrashing continued, albeit at a more subdued pace. The angler fish and the classical mers watched patiently, but you could see that all of them were transfixed by the sight of their comrade overcoming the pain. Usually, at this point, you would often pinpoint the exact moment a mer would decide to give up and discontinue the program. It was unfortunate, but not everyone was suited to the surface.

If your suspicions were correct, one of the classic mers looked like she was about to be sick while the other two merfolk were eyeing their vials with a scrutinizing gaze.

After giving them a warning glance, to which all of them merely nodded, you focused once more on the cecaelia. His breathing had regulated and the grip he had on the boardwalk was less desperate and more out of desire to seek stability.

You bent down low, murmuring to him so as to not startle him out of how much of a good job he was doing. "Most cecaelia have a hard time adjusting to losing six of their limbs. Can you raise your right arm—" You gestured to your own arm to inform him you meant the human appendage and not what most humans would mistakenly call a tentacle. "—and spread your fingers wide?"

The cecaelia's eyes widened, his lips wobbling. Out of breath and clearly out of his element, he experimentally tried to move... something, but his mind must have been too frazzled other than to cast his gaze downward, clearly at a loss. That was okay. Shelley had felt the same. Both the pain and the shock of losing so much oneself within seconds was hard, but it was even harder trying to control and relearn how to move and be at peace with what you had left.

Another note that you would have to make in the margin of your roster. The cecaelia would need time to adjust. Within a month, that was more than fine. All of the merfolk were permitted time out of their busy schedules to practice and train their muscles.

Reaching behind you, you picked up one of the floating devices that were assigned to you. There were a series of life jackets, buoyancy paddles, and pool noodles. Some mers wanted to get acclimated to wearing human clothing (so they chose the life jackets), others wanted something that was not restraining and had the familiarity of floating driftwood (hence the paddles and noodles).

"Would you like a life jacket?"

Bright blue eyes the color of the calm ocean glared dangerously at you. Had you not had your fair share of predatory mer, you would have found yourself almost scared. However, you could understand the feeling of getting one's pride crushed, even if it was the first time gaining legs. Still, though, such anger seemed simultaneously out of place, yet right at home, on his pale face.

"One of those, please." He concentrated on his right arm, but after realizing that he was still somewhat stuck in his current mindset and still working out the connections, he glanced at the noodles that you kept at your side.

It took some maneuvering and some help from the angler fish (you saw that the cecaelia had a hard time being so close to a predator when he was now human), but you managed to get the pool noodle under his arms so that he could float without any more aid.

Satisfied that he wouldn't recklessly flip over and risk drowning himself, you called out the second name on your list.

It was going to be one long session.

.

.

.

[PART ONE HERE] [PART TWO] [PART THREE] [PART FOUR]

If you want to donate a Ko-Fi, feel free https://ko-fi.com/devintrinidad.

TWISTED WONDERLAND MASTERLIST

[uthra-krish]

The Skills I Acquired on My Path to Becoming a Data Scientist

Data science has emerged as one of the most sought-after fields in recent years, and my journey into this exciting discipline has been nothing short of transformative. As someone with a deep curiosity for extracting insights from data, I was naturally drawn to the world of data science. In this blog post, I will share the skills I acquired on my path to becoming a data scientist, highlighting the importance of a diverse skill set in this field.

The Foundation — Mathematics and Statistics

At the core of data science lies a strong foundation in mathematics and statistics. Concepts such as probability, linear algebra, and statistical inference form the building blocks of data analysis and modeling. Understanding these principles is crucial for making informed decisions and drawing meaningful conclusions from data. Throughout my learning journey, I immersed myself in these mathematical concepts, applying them to real-world problems and honing my analytical skills.

Programming Proficiency

Proficiency in programming languages like Python or R is indispensable for a data scientist. These languages provide the tools and frameworks necessary for data manipulation, analysis, and modeling. I embarked on a journey to learn these languages, starting with the basics and gradually advancing to more complex concepts. Writing efficient and elegant code became second nature to me, enabling me to tackle large datasets and build sophisticated models.

Data Handling and Preprocessing

Working with real-world data is often messy and requires careful handling and preprocessing. This involves techniques such as data cleaning, transformation, and feature engineering. I gained valuable experience in navigating the intricacies of data preprocessing, learning how to deal with missing values, outliers, and inconsistent data formats. These skills allowed me to extract valuable insights from raw data and lay the groundwork for subsequent analysis.

Data Visualization and Communication

Data visualization plays a pivotal role in conveying insights to stakeholders and decision-makers. I realized the power of effective visualizations in telling compelling stories and making complex information accessible. I explored various tools and libraries, such as Matplotlib and Tableau, to create visually appealing and informative visualizations. Sharing these visualizations with others enhanced my ability to communicate data-driven insights effectively.

Machine Learning and Predictive Modeling

Machine learning is a cornerstone of data science, enabling us to build predictive models and make data-driven predictions. I delved into the realm of supervised and unsupervised learning, exploring algorithms such as linear regression, decision trees, and clustering techniques. Through hands-on projects, I gained practical experience in building models, fine-tuning their parameters, and evaluating their performance.

Database Management and SQL

Data science often involves working with large datasets stored in databases. Understanding database management and SQL (Structured Query Language) is essential for extracting valuable information from these repositories. I embarked on a journey to learn SQL, mastering the art of querying databases, joining tables, and aggregating data. These skills allowed me to harness the power of databases and efficiently retrieve the data required for analysis.

Domain Knowledge and Specialization

While technical skills are crucial, domain knowledge adds a unique dimension to data science projects. By specializing in specific industries or domains, data scientists can better understand the context and nuances of the problems they are solving. I explored various domains and acquired specialized knowledge, whether it be healthcare, finance, or marketing. This expertise complemented my technical skills, enabling me to provide insights that were not only data-driven but also tailored to the specific industry.

Soft Skills — Communication and Problem-Solving

In addition to technical skills, soft skills play a vital role in the success of a data scientist. Effective communication allows us to articulate complex ideas and findings to non-technical stakeholders, bridging the gap between data science and business. Problem-solving skills help us navigate challenges and find innovative solutions in a rapidly evolving field. Throughout my journey, I honed these skills, collaborating with teams, presenting findings, and adapting my approach to different audiences.

Continuous Learning and Adaptation

Data science is a field that is constantly evolving, with new tools, technologies, and trends emerging regularly. To stay at the forefront of this ever-changing landscape, continuous learning is essential. I dedicated myself to staying updated by following industry blogs, attending conferences, and participating in courses. This commitment to lifelong learning allowed me to adapt to new challenges, acquire new skills, and remain competitive in the field.

In conclusion, the journey to becoming a data scientist is an exciting and dynamic one, requiring a diverse set of skills. From mathematics and programming to data handling and communication, each skill plays a crucial role in unlocking the potential of data. Aspiring data scientists should embrace this multidimensional nature of the field and embark on their own learning journey. If you want to learn more about Data science, I highly recommend that you contact ACTE Technologies because they offer Data Science courses and job placement opportunities. Experienced teachers can help you learn better. You can find these services both online and offline. Take things step by step and consider enrolling in a course if you’re interested. By acquiring these skills and continuously adapting to new developments, they can make a meaningful impact in the world of data science.

[accelebrate]

Machine Learning Training for Non-Tech Professionals: How to Get Started

For non-tech professionals, venturing into machine learning can seem intimidating. Yet, this field is becoming more accessible, presenting exciting opportunities for those ready to tackle new challenges. This guide will help you navigate the transition into machine learning roles, providing you with the essential knowledge and resources to start your journey with confidence. By following these steps, you'll be well on your way to harnessing the power of machine learning to enhance your career.

Understanding the Basics

Before you jump into the deep end, it's essential to grasp the fundamentals of machine learning. Start with understanding what machine learning is: it's a branch of artificial intelligence (AI) that involves training algorithms to learn from data and make predictions or decisions without being explicitly programmed. The core concepts include supervised learning, unsupervised learning, and reinforcement learning. Familiarizing yourself with these basics will provide a solid foundation for further exploration.

Identify Your Goals

Next, define why you want to learn machine learning. Are you looking to enhance your current role, switch careers, or start a new project? Knowing your goals will help you choose the right learning path. For instance, if you're in marketing, you might focus on predictive analytics. If you're in finance, you might be more interested in risk modeling.

Leverage Online Resources

There is a wealth of online resources designed to make machine learning accessible to non-tech professionals. Platforms like Coursera, edX, and Udacity offer introductory courses that cover the basics of machine learning without requiring a deep technical background. Look for courses that offer practical exercises and real-world applications, which can help bridge the gap between theory and practice.

Start with Data Analysis

One of the most crucial aspects of machine learning is data analysis. Learning how to handle and analyze data will make the transition smoother. Tools like Excel, Google Sheets, and basic statistical software are great starting points. Once you're comfortable with data handling, you can move on to more advanced tools like Python or R, which are commonly used in machine learning.

Choose the Right Training Program

For non-tech professionals, choosing a comprehensive and supportive training program is vital. Look for programs that offer structured learning paths, hands-on projects, and personalized support. Accelebrate is a renowned provider in this space, offering a wide range of courses designed to cater to different levels of expertise and industry needs. Their courses are known for their practical approach and expert instructors, making them an excellent choice for those new to the field.

Stay Updated and Keep Learning

Machine learning is a rapidly evolving field, and continuous learning is essential. Stay updated with the latest trends, tools, and technologies by following industry blogs, subscribing to relevant journals, and experimenting with new techniques and algorithms.

In Conclusion

Transitioning into a machine learning role as a non-tech professional is entirely achievable with the right approach and resources. To further accelerate your learning journey, consider enrolling in a course with Accelebrate. Their extensive range of machine learning courses is tailored to help professionals from all backgrounds gain the skills they need to succeed in the world of data and AI.

Embrace the challenge, and you'll find that the world of machine learning is not just for techies—it's for anyone willing to learn and innovate. Happy learning!

For more details, visit: https://www.accelebrate.com/machine-learning-training

[almea]

I've got 2.1 thoughts.

This was really the Aventurine patch. His perspective was definitely the longest one, especially considering he's the first one you explore both maps with. It's an interesting way to tell the story because with the kind of person Aventurine is, there's no way he would have confided even half of what we learned about him in the story to another character. I didn't dislike him after 2.0 (Although I did find it funny when he was trying to make us suspicious of Acheron because she's an Emanator and I was just like. Aventurine, you just told me that not only is Acheron hot but she's also scary strong and you expect me to not lay my life on the line for her?) but I wasn't that invested in him either. I'm still not Invested invested in him after 2.1, but I did really feel for him, especially towards the end. I'm just truly the weakest bitch in the universe when it comes to characters talking to their younger selves. I didn't expect them to get as into Aventurine being a slave or former slave as they did, but damn.

And by the end I was like. Is this man dead? Did they kill this man right before his banner? After everyone was like "Oh no, they drip marketed Robin so now everyone knows she's not dead because dead characters wouldn't be playable"? After his talk with Acheron my understanding is that he's not actually dead, he was just able to break in to a deeper part of dream because of Acheron and he may not be able to return so I guess he's effectively kind of dead for now???

I think it's such a weird choice that Aventurine's hat is clearly supposed to be important to him but he just. Doesn't wear it 95% of the time???? I feel like that final cutscene would have hit harder if he actually wore his hat so it would be more strongly associated with him. I don't understand why they designed him with that hat and then just like never make him wear it.

It's pretty clever that his perspective being so long also acts as an extra long trial for his character. I ended up using my Jingliu team for his portions of the story because she's the only DPS that I've even remotely built besides Qingque, and she was truly doing more damage to my team than the enemies were. Thinking about it now, I prooooobably would have been fine running him with my Qingque team but I would have missed the reliability of the mono Quantum. The extra long trial did not work on me though because I've got Stellaron Hunters to collect and I'm still waffling about whether or not to get Acheron's light cone. I also still haven't made up my mind about whether to pull for Robin yet.

His boss fight was kind of a pain in the ass though. My poor Silver Wolf and Lynx were getting bullied nonstop during the second phase while Qingque was overcapping the dice like crazy because she kept getting her follow up and Fu Xuan's ultimate was her saving grace. Using Qingque against Aventurine feels so appropriate because so many of her combat lines are related to gambling.

I liked Sparkle's conversation with Aventurine very plainly confirming that she has a very clear picture of what's happening but, true to her little animation, she prefers to work from the background and let the main characters do all the heavy lifting. Going off that animation, I'm looking forward to seeing what role she plays in 2.2.

March being so indignant that Stelle was getting bullied left and right by people with Agendas ever since they split up was really sweet. I love how people's general feeling after 2.0 was like "Everyone we met was fucking suspicious and hiding things from us, I don't fully trust anyone except the rest of the Express crew" and the characters were also like "Yeah, we can only trust each other."

The other side of that being that they kept running into people Stelle fought in 2.0 and March and Himeko being like "What have you been doing while you were unsupervised? Were you committing crimes?" was really funny.

I'm curious about whether Dan Heng is going to join in on the action in 2.2. He's already asked if they they need him in 2.1, so the possibility of him getting called in if things get really dicey is already there. And there's the fact that he does appear in one shot of the White Night trailer, and I watched a stream where his voice actor was saying Penacony's really good which makes it sound like he recorded more lines than just Dan Heng saying he'd stay on the train. Or idk maybe the other voice actors told him about stuff that happened. Is that allowed lmao? Sam also said Elio's instruction was "Get all of the Astral Express to track down the grand legacy," so does that "all" include Dan Heng?

It was very cruel of them to have one of the first things in the story be Acheron saying she knows who's in Sam's armour and then just. Not touching on that again until the last five to ten minutes. I figured they were going to do the Sam/Firefly reveal at some point in 2.1 so they'd be able to drip market Sam/Firefly for 2.3 (I've deluded myself into believing they're coming out in 2.3 because it makes sense to me to release them during Penacony's epilogue while they're main story relevant) since they're probably both in the splash art, but man did they take their sweet time getting there and slowly killed me the entire time.

I'm sooooo fascinated by the whole Sam/Firefly thing. I was tragically spoiled by a leak forever ago, but the leak I saw was just someone saying "I guess Firefly is the exploration model and Sam is the battle model" so I still don't know whether Firefly is an actual person who exists in the real world or if she's just a form Sam is able to take on in the dream world. If Firefly is a real person, I am so obsessed with the juxtaposition of Sam being the most ruthless Stellaron Hunter, to the point where Kafka said people are better off running into her, and Firefly looking like the sweetest, most gentle girl ever.

And if she's real, I'm so curious about whether the other Stellaron Hunters know about Firefly. They've only ever referred to Sam with male pronouns, but we've never had a scene with just them where they mentioned Sam so they could know about her and have Reasons for letting other people think Sam is male.

Looking back at 2.0, I assume Firefly might have transformed into Sam if Black Swan hadn't saved them because Stelle was in danger and their scripts from Elio aren't as detailed as the ones we've seen the other Stellaron Hunters reference, and her apology when she was killed could be read as "sorry I'm going to give you some trauma now so you'll have the motivation to find the Watchmaker's legacy." I'm not super clear on whether the memories of Firefly Stelle followed with Black Swan and Acheron was just an act she put on to lead Stelle to that place or there was something more going on there.

I just really want to know how much of Firefly in 2.0 was an act and how much of it was genuine. The main thing being when she tells Stelle she hopes they don't have to be enemies when everything is revealed. I love how Firefly's Sam persona was clearly leaking out a bit when she told Sampo/Sparkle "You talk too much."

2.2 really cannot come soon enough because I've got so many questions and I'm so excited to see the story pick up right from Stelle finding out Firefly is Sam. I can't wait to see what dialogue options Stelle gets for her reaction. Stelle's thoughts when you go back to Firefly's secret base are that she still cherishes the memories of the time she spent with Firefly, but she's confused about how to feel about her being Sam. I'm just like, Stelle, please hug her because I am a fool who would forgive her because she's cute and I love the Stellaron Hunters dearly, but I think if I'm not being self-indulgent I'm just expecting Stelle to be happy or relieved that Firefly's not dead but also feel very wary of her.

I think it's so funny that during the 2.1 livestream they were like. "Gallagher is so normal. He's the most normal person in the whole Penacony cast. He doesn't have a past, he's just a guy." And then by the end of 2.1 it's like. Actually Gallagher may have the biggest past out of everyone.

I'm not clear on what's up with that bird that's watching in some scenes. I thought it could be related to Sunday since he said he has servants that see everything, but its colour scheme looks like the Memory Zone monsters so it might also be related to Gallagher? idk if I'm even supposed to understand at this point.

I love that they made a special trial for Acheron so they could be like "Look how cool her technique is!!!"

It was very nice of them to let us collect the birds even when we weren't in Stelle's perspective, but Acheron and Aventurine can't actually see them so the dialogue options reflected that.

Black Swan talking to a memory? Of Constance wasn't something I expected. I'm still not clear on what was happening during that scene, but I'm interested to see where it goes in 2.2. Boothill getting an early cameo in that part was fun as well. It was so funny to me when he got drip marketed because I was just like. I have never seen this man in my life before lmao. I must have stopped looking at leaks before he was found.

Topaz's very brief appearances were very interesting. It seems like she feels at least a little conflicted about Aventurine's "death." I think Topaz herself is very kind, but she's still one of the Ten Stonehearts and that whole group seems a little shady. I don't think I trust Jade at all.

[vivekavicky12]

Navigating the Data Science Learning Landscape: A Guide to Different Types of Courses

Embarking on a journey into the realm of data science involves mastering a diverse set of skills. Whether you're a beginner or looking to specialize, understanding the types of data science courses available is crucial. Choosing the best Data Science Institute can further accelerate your journey into this thriving industry.

In this blog, we'll navigate through various types of data science courses, each catering to specific facets of this multidimensional field. 

1. Foundational Data Science Courses:

Foundational courses lay the groundwork for understanding key concepts in data science. They cover fundamental principles of data analysis, statistics, and basic programming skills necessary for any data scientist.

2. Programming for Data Science Courses:

Mastery of programming languages is at the core of data science. Courses in this category focus on teaching languages such as Python or R, ensuring proficiency in the tools essential for data manipulation and analysis.

3. Data Visualization Courses:

Data visualization is an art form in data science. These courses delve into techniques for creating compelling visualizations that effectively communicate insights drawn from data.

4. Machine Learning Courses:

Machine learning is a cornerstone of data science. Courses in this category explore various algorithms and models used in machine learning, covering both supervised and unsupervised learning techniques.

5. Deep Learning Courses:

For those diving into the intricacies of neural networks and deep learning, specialized courses explore frameworks, applications, and the theoretical underpinnings of this powerful subset of machine learning.

6. Big Data Courses:

Handling large volumes of data requires specialized skills. Big data courses address the challenges and tools associated with processing and analyzing massive datasets.

7. Natural Language Processing (NLP) Courses:

Understanding and processing human language is critical in data science. NLP courses focus on techniques for working with text and language-related data.

8. Data Engineering Courses:

Data engineering courses cover the technical aspects of collecting, storing, and managing data to ensure it's ready for analysis.

9. Time Series Analysis Courses:

For those working with time-dependent data, time series analysis courses provide insights into techniques for analyzing and forecasting temporal patterns.

10. Data Ethics and Privacy Courses:

As data science continues to evolve, ethical considerations become paramount. Courses in data ethics and privacy address the responsible handling of data and the associated ethical considerations.

11. Domain-Specific Data Science Courses:

Tailored to specific industries or applications, these courses delve into the unique challenges and opportunities within domains such as healthcare, finance, or marketing.

12. Capstone Projects or Case Studies:

Application-focused courses allow learners to bring together their skills by working on real-world projects or case studies. This hands-on experience is invaluable for showcasing practical expertise.

In the vast landscape of data science, the journey of learning involves a variety of courses catering to different skill sets and interests. Whether you're building a strong foundation, specializing in a specific area, or applying your skills to real-world projects, the diverse types of data science courses ensure there's a learning path for everyone. Choose courses based on your current level, career aspirations, and the specific aspects of data science that intrigue you the most. Remember, the key to mastering data science lies in the continuous pursuit of knowledge and hands-on experience. Choosing the best Data Science courses in Chennai is a crucial step in acquiring the necessary expertise for a successful career in the evolving landscape of data science.

[redbixbite-solutions]

Everything You Need to Know About Machine Learning

Ready to step into the world of possibilities with machine learning? Learn all about machine learning and its cutting-edge technology. From what do you need to learn before using it to where it is applicable and their types, join us as we reveal the secrets. Read along for everything you need to know about Machine Learning!

What is Machine Learning?

Machine Learning is a field of study within artificial intelligence (AI) that concentrates on creating algorithms and models which enable computers to learn from data and make predictions or decisions without being explicitly programmed. The process involves training a computer system using copious amounts of data to identify patterns, extract valuable information, and make precise predictions or decisions.

Fundamentally, machine Learning relies on statistical techniques and algorithms to analyze data and discover patterns or connections. These algorithms utilize mathematical models to process and interpret data. Revealing significant insights that can be utilized across various applications by different AI ML services.

What do you need to know for Machine Learning?

You can explore the exciting world of machine learning without being an expert mathematician or computer scientist. However, a  basic understanding of statistics, programming, and data manipulation will benefit you. Machine learning involves exploring patterns in data, making predictions, and automating tasks.

 It has the potential to revolutionize industries. Moreover, it can improve healthcare and enhance our daily lives. Whether you are a beginner or a seasoned professional embracing machine learning can unlock numerous opportunities and empower you to solve complex problems with intelligent algorithms.

Types of Machine Learning

Let’s learn all about machine learning and know about its types.

Supervised Learning

Supervise­d learning resemble­s having a wise mentor guiding you eve­ry step of the way. In this approach, a machine le­arning model is trained using labele­d data wherein the de­sired outcome is already known.

The­ model gains knowledge from the­se provided example­s and can accurately predict or classify new, unse­en data. It serves as a highly e­ffective tool for tasks such as dete­cting spam, analyzing sentiment, and recognizing image­s.

Unsupervised Learning

In the re­alm of unsupervised learning, machine­s are granted the autonomy to e­xplore and unveil patterns inde­pendently. This methodology mainly ope­rates with unlabeled data, whe­re models strive to une­arth concealed structures or re­lationships within the information.

It can be likene­d to solving a puzzle without prior knowledge of what the­ final image should depict. Unsupervise­d learning finds frequent application in dive­rse areas such as clustering, anomaly de­tection, and recommendation syste­ms.

Reinforcement Learning

Reinforce­ment learning draws inspiration from the way humans le­arn through trial and error. In this approach, a machine learning mode­l interacts with an environment and acquire­s knowledge to make de­cisions based on positive or negative­ feedback, refe­rred to as rewards.

It's akin to teaching a dog ne­w tricks by rewarding good behavior. Reinforce­ment learning finds exte­nsive applications in areas such as robotics, game playing, and autonomous ve­hicles.

Machine Learning Process

Now that the diffe­rent types of machine le­arning have been e­xplained, we can delve­ into understanding the encompassing proce­ss involved.

To begin with, one­ must gather and prepare the­ appropriate data. High-quality data is the foundation of any triumph in a machine le­arning project.

Afterward, one­ should proceed by sele­cting an appropriate algorithm or model that aligns with their spe­cific task and data type. It is worth noting that the market offe­rs a myriad of algorithms, each possessing unique stre­ngths and weaknesses.

Next, the machine goes through the training phase. The model learns from making adjustments to its internal parameters and labeled data. This helps in minimizing errors and improves its accuracy.

Evaluation of the machine’s performance is a significant step. It helps assess machines' ability to generalize new and unforeseen data. Different types of metrics are used for the assessment. It includes measuring accuracy, recall, precision, and other performance indicators.

The last step is to test the machine for real word scenario predictions and decision-making. This is where we get the result of our investment. It helps automate the process, make accurate forecasts, and offer valuable insights. Using the same way. RedBixbite offers solutions like DOCBrains, Orionzi, SmileeBrains, and E-Governance for industries like agriculture, manufacturing, banking and finance, healthcare, public sector and government, travel transportation and logistics, and retail and consumer goods.

Applications of Machine Learning

Do you want to know all about machine learning? Then you should know where it is applicable.

Natural Language Processing (NLP)- One are­a where machine le­arning significantly impacts is Natural Language Processing (NLP). It enables various applications like­ language translation, sentiment analysis, chatbots, and voice­ assistants. Using the prowess of machine le­arning, NLP systems can continuously learn and adapt to enhance­ their understanding of human language ove­r time.

Computer Vision- Computer Vision pre­sents an intriguing application of machine learning. It involve­s training computers to interpret and compre­hend visual information, encompassing images and vide­os. By utilizing machine learning algorithms, computers gain the­ capability to identify objects, faces, and ge­stures, resulting in the de­velopment of applications like facial re­cognition, object detection, and autonomous ve­hicles.

Recommendation Systems- Recomme­ndation systems have become­ an essential part of our eve­ryday lives, with machine learning playing a crucial role­ in their developme­nt. These systems care­fully analyze user prefe­rences, behaviors, and patte­rns to offer personalized re­commendations spanning various domains like movies, music, e­-commerce products, and news article­s.

Fraud Detection- Fraud dete­ction poses a critical concern for businesse­s. In this realm, machine learning has e­merged as a game-change­r. By meticulously analyzing vast amounts of data and swiftly detecting anomalie­s, machine learning models can ide­ntify fraudulent activities in real-time­.

Healthcare- Machine learning has also made great progress in the healthcare sector. It has helped doctors and healthcare professionals make precise and timely decisions by diagnosing diseases and predicting patient outcomes. Through the analysis of patient data, machine learning algorithms can detect patterns and anticipate possible health risks, ultimately resulting in early interventions and enhanced patient care.

In today's fast-paced te­chnological landscape, the field of artificial inte­lligence (AI) has eme­rged as a groundbreaking force, re­volutionizing various industries. As a specialized AI de­velopment company, our expe­rtise lies in machine le­arning—a subset of AI that entails creating syste­ms capable of learning and making predictions or de­cisions without explicit programming.

Machine learning's wide­spread applications across multiple domains have transforme­d businesses' operations and significantly e­nhanced overall efficie­ncy.

[threewaysdivided]

G.R.R. Martin once planned to have a time skip in between each chapter of the books, but discarded it because it would require large amounts of flashbacks and expositions to get the audience up to speed. Boy, it sure is great that YJ only had one season so we didn't have to worry about each sequential season having to allocate precious episode time to explain what happen in the gaps while also developing new characters and plot lines /s

Sadly I cannot speak to or for Mister George Raymond Richard Martin's storytelling as I managed to miss the boat on both ASOIAF as a book series and Game of Thrones as a TV phenomenon. However, it certainly seems like he made the right decision there. Smart man.

As for the Young Justice Animated series... I can only agree. While I would have loved to see a continuation of the mystery, and think there was a lot of potential in the original cast of heroes and interesting things they could have done with The Light, I'm glad that DC made the call to cancel the show and move on to new projects when it became clear that the tone and themes didn't fit with their brand-pivot to New 52 edginess, rather than trying to forcibly crush the series into an incompatible mold for use as a marketing vessel.

Plus, the more complaints I've heard from other franchises about what happens when you leave Greg Weisman creatively unsupervised, the more likely it is that we would have just been in for a bad-fanfic barrage of random new characters, arbitrary time-skips, jumbled perspective-hopping, attempted twitter-retcons and general throwing-ideas-at-a-wall that he seems to use in place of actually learning basic narrative techniques like developing a story arc, maintaining character consistency or creating proper causal links between concepts, all seasoned with a heaping helping of Joss-Whedon-inspired performative virtue-signalling, creepy rape-y garbage and fetishistic sexism. Bullet dodged, methinks. Our city now.

There are fine things that are more brilliant when they are unfinished than when finished too much. - François de La Rochefoucauld

Here's to a rare and pleasant display of creative honesty.

[Goof-posting because there's really not a lot about YJ's problems that I haven't already dissected at length. If you want a serious technical look at the scope and structure problems the timeskips cause then I've gone into it here. You can also try here for a deeper dive into how the timeskips are actually just a symptom of a bigger problem of narratively directionless story-contradictions being hastily papered over by a largely-untalented privilege-poisoned embodiment of George Lucas Syndrome and a largely-inexperienced adaptation producer who didn't have the original-storytelling chops to compensate for his co-runner's bullshit once the actually talented original production/ directorial team left the picture. C'est la capitalisme, I guess.]

[idiotdriftinginspace]

TOTALY WORTH IT!

no...

ABSOLUTLEY NO REGRETS!

Ok, so if anyone somehow stumbles upon this out of context scene from my (weird, separated?) AU i've been developing since ... november or december wow, here some context, it will be needed:

Tthese 3 idiots went to the Witch Town for some rocks and metal bars, or rather Venus did and draged the other two with her despite their complains. On top of that she left them unsupervised on the main market for as little as 15 minutes. Big mistake. Because Frida is extremly passionate about space and astrophisics and Donnie about science, it was a matter of minutes before they'd try reasoning with them and later start insulting and shortly after start a fight with flatearthers and such*.

They have absolutley zero regrets and they're pround of themselves.

*(my hc that the witches absolutley despise scientists and science)

I'm curently working on a more cleaner version, so when i finish it i'll post it and leave a link here.

Edit: so thanks to the recent Twitter QnA i have learned that the Big Mamas assistant aka one of the missing sisters name could have been Frida Kahlo, and i think this name fits her perfectly, do a little eddit

The Twitter Rise QnA:

The tumbler post with info:

[oraahdamaanta]

what is Artificial Intelligence AI

Artificial Intelligence (AI) is a field of computer science that focuses on the development of machines and software that can perform tasks that typically require human intelligence, such as perception, reasoning, learning, decision-making, and natural language processing. AI systems are designed to analyse data, recognize patterns, and make predictions or decisions based on that data. The goal of AI is to create machines that can think, reason, and learn like humans, and to develop intelligent systems that can automate tasks and improve our lives in various ways. 

How Does AI Work

AI systems are designed to analyze and process vast amounts of data in real-time and derive insights from that data to make predictions or take actions. There are three main types of AI systems: rule-based systems, machine learning, and deep learning. 

  1.Rule-Based Systems 

Rule-based systems are the simplest form of AI and rely on a set of pre-defined rules to make decisions. These systems work by analyzing data and applying specific rules to make decisions. For example, a rule-based system might be programmed to diagnose a disease based on a set of symptoms. The system would analyze the data, apply the rules, and then provide a diagnosis based on the outcome. 

   2.Machine Learning 

Machine learning is a more advanced form of AI that involves teaching machines to learn from data without being explicitly programmed. This is done through a process known as training, in which machines are fed large amounts of data and algorithms are used to identify patterns and learn from the data. machinelearningmastery/ The machine then applies what it has learned to new data to make predictions or take actions. 

There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. 

Supervised Learning: Supervised learning is a type of machine learning where the algorithm learns to make predictions by analyzing labelled training data. In this type of learning, the algorithm is trained using input-output pairs, where the input is a set of features or attributes, and the output is a labelled target value. The goal of supervised learning is to find a mapping between the input and output variables so that the algorithm can accurately predict the output for new, unseen inputs. 

Examples of supervised learning include predicting the price of a house based on its features or diagnosing a patient’s illness based on their symptoms. 

Unsupervised Learning: Unsupervised learning is a type of machine learning where the algorithm learns to identify patterns and relationships in unlabelled data. In unsupervised learning, the algorithm is given a set of inputs without any corresponding output labels, and it must find patterns or groupings in the data on its own. 

Examples of unsupervised learning include identifying groups of similar customers in a marketing dataset or finding patterns in unstructured text data. 

[bayesic-bitch]

I agree with 90% of this but with a couple points of disagreement.

both will be integrated into production pipelines in ways that put people out of jobs or justify lower pay for existing jobs

This is certainly a possibility but I don't think it's the most likely one. Generally increasing the capital workers use (which is how we should view image generation tech) leads to fewer jobs at higher wages with shorter hours in the long run. Bulldozer drivers make considerably more per hour than guys with shovels, because they're able to move more dirt per hour. But fewer total people are employed after this transition, because the new tools enable the work to be done by fewer people. The wages of workers after this transition can vary, but right now we have a historically tight labor market, so it's happening under favorable conditions.

Personally, I see the tradeoff of "fewer workers in better conditions" to be a positive one in many areas that would be most affected. I'm mainly thinking of game development, where crunch time frequently means 60-80 hour weeks of touching up hyperrealistic gun models and ultra-detailed flaking wall paint. That's exactly the kind of task that would be perfect to automate, and lets game artists shift more into the role of an art director, of guiding, supervising, and coordinating the outputs of image models.

the process of training AIs and labelling datasets involves profound exploitation of workers in the global south

Still kinda true, but I suspect it's much less true now than it was 5-10 years ago. LLMs and diffusion networks are both primarily trained in an unsupervised manner, which use an unlabelled dataset. Companies now typically start with a large pre-trained model and fine-tune it on in-house data, so my understanding is that the role of unskilled data-labellers has significantly diminished. I could be wrong about this, I had difficulty finding much data.

the ability of AI tech to automate biases while erasing accountability is chilling.

This is another area that's gotten somewhat better over time. Heavy press coverage on the issue means that generally people know better now than to train on overtly biased data (eg, predicting judges' sentences after trials). Machine learning conferences often require statements considering the potential for bias and its impact in submitted papers and will reject work if this is lacking (side note but this is very annoying in robotics. my robot is too stupid to stand up and too stupid to be racist). Recent research has also made good progress on this, there are some ways to go through and remove bias from models. Unlike people, you can do brain surgery on a neural net to make it less biased.

bing ai wont let me generate 'tesla CEO meat mistake' because it hates fun

Completely true, no notes. Need to get my local open-source instance of stable-diffusion-2 running to bypass this blatant overreach.

are there any critiques of AI art or maybe AI in general that you would agree with?

AI art makes it a lot easier to make bad art on a mass production scale which absolutely floods art platforms (sucks). LLMs make it a lot easier to make content slop on a mass production scale which absolutely floods search results (sucks and with much worse consequences). both will be integrated into production pipelines in ways that put people out of jobs or justify lower pay for existing jobs. most AI-produced stuff is bad. the loudest and most emphatic boosters of this shit are soulless venture capital guys with an obvious and profound disdain for the concept of art or creative expression. the current wave of hype around it means that machine learning is being incorporated into workflows and places where it provides no benefit and in fact makes services and production meaningfully worse. it is genuinely terrifying to see people looking to chatGPT for personal and professional advice. the process of training AIs and labelling datasets involves profound exploitation of workers in the global south. the ability of AI tech to automate biases while erasing accountability is chilling. seems unwise to put a lot of our technological basket in a completely opaque black box basket (mixing my metaphors ab it with that one). bing ai wont let me generate 'tesla CEO meat mistake' because it hates fun

[uthra-krish]

From Curious Novice to Data Enthusiast: My Data Science Adventure

I've always been fascinated by data science, a field that seamlessly blends technology, mathematics, and curiosity. In this article, I want to take you on a journey—my journey—from being a curious novice to becoming a passionate data enthusiast. Together, let's explore the thrilling world of data science, and I'll share the steps I took to immerse myself in this captivating realm of knowledge.

The Spark: Discovering the Potential of Data Science

The moment I stumbled upon data science, I felt a spark of inspiration. Witnessing its impact across various industries, from healthcare and finance to marketing and entertainment, I couldn't help but be drawn to this innovative field. The ability to extract critical insights from vast amounts of data and uncover meaningful patterns fascinated me, prompting me to dive deeper into the world of data science.

Laying the Foundation: The Importance of Learning the Basics

To embark on this data science adventure, I quickly realized the importance of building a strong foundation. Learning the basics of statistics, programming, and mathematics became my priority. Understanding statistical concepts and techniques enabled me to make sense of data distributions, correlations, and significance levels. Programming languages like Python and R became essential tools for data manipulation, analysis, and visualization, while a solid grasp of mathematical principles empowered me to create and evaluate predictive models.

The Quest for Knowledge: Exploring Various Data Science Disciplines

A. Machine Learning: Unraveling the Power of Predictive Models

Machine learning, a prominent discipline within data science, captivated me with its ability to unlock the potential of predictive models. I delved into the fundamentals, understanding the underlying algorithms that power these models. Supervised learning, where data with labels is used to train prediction models, and unsupervised learning, which uncovers hidden patterns within unlabeled data, intrigued me. Exploring concepts like regression, classification, clustering, and dimensionality reduction deepened my understanding of this powerful field.

B. Data Visualization: Telling Stories with Data

In my data science journey, I discovered the importance of effectively visualizing data to convey meaningful stories. Navigating through various visualization tools and techniques, such as creating dynamic charts, interactive dashboards, and compelling infographics, allowed me to unlock the hidden narratives within datasets. Visualizations became a medium to communicate complex ideas succinctly, enabling stakeholders to understand insights effortlessly.

C. Big Data: Mastering the Analysis of Vast Amounts of Information

The advent of big data challenged traditional data analysis approaches. To conquer this challenge, I dived into the world of big data, understanding its nuances and exploring techniques for efficient analysis. Uncovering the intricacies of distributed systems, parallel processing, and data storage frameworks empowered me to handle massive volumes of information effectively. With tools like Apache Hadoop and Spark, I was able to mine valuable insights from colossal datasets.

D. Natural Language Processing: Extracting Insights from Textual Data

Textual data surrounds us in the digital age, and the realm of natural language processing fascinated me. I delved into techniques for processing and analyzing unstructured text data, uncovering insights from tweets, customer reviews, news articles, and more. Understanding concepts like sentiment analysis, topic modeling, and named entity recognition allowed me to extract valuable information from written text, revolutionizing industries like sentiment analysis, customer service, and content recommendation systems.

Building the Arsenal: Acquiring Data Science Skills and Tools

Acquiring essential skills and familiarizing myself with relevant tools played a crucial role in my data science journey. Programming languages like Python and R became my companions, enabling me to manipulate, analyze, and model data efficiently. Additionally, I explored popular data science libraries and frameworks such as TensorFlow, Scikit-learn, Pandas, and NumPy, which expedited the development and deployment of machine learning models. The arsenal of skills and tools I accumulated became my assets in the quest for data-driven insights.

The Real-World Challenge: Applying Data Science in Practice

Data science is not just an academic pursuit but rather a practical discipline aimed at solving real-world problems. Throughout my journey, I sought to identify such problems and apply data science methodologies to provide practical solutions. From predicting customer churn to optimizing supply chain logistics, the application of data science proved transformative in various domains. Sharing success stories of leveraging data science in practice inspires others to realize the power of this field.

Cultivating Curiosity: Continuous Learning and Skill Enhancement

Embracing a growth mindset is paramount in the world of data science. The field is rapidly evolving, with new algorithms, techniques, and tools emerging frequently. To stay ahead, it is essential to cultivate curiosity and foster a continuous learning mindset. Keeping abreast of the latest research papers, attending data science conferences, and engaging in data science courses nurtures personal and professional growth. The journey to becoming a data enthusiast is a lifelong pursuit.

Joining the Community: Networking and Collaboration

Being part of the data science community is a catalyst for growth and inspiration. Engaging with like-minded individuals, sharing knowledge, and collaborating on projects enhances the learning experience. Joining online forums, participating in Kaggle competitions, and attending meetups provides opportunities to exchange ideas, solve challenges collectively, and foster invaluable connections within the data science community.

Overcoming Obstacles: Dealing with Common Data Science Challenges

Data science, like any discipline, presents its own set of challenges. From data cleaning and preprocessing to model selection and evaluation, obstacles arise at each stage of the data science pipeline. Strategies and tips to overcome these challenges, such as building reliable pipelines, conducting robust experiments, and leveraging cross-validation techniques, are indispensable in maintaining motivation and achieving success in the data science journey.

Balancing Act: Building a Career in Data Science alongside Other Commitments

For many aspiring data scientists, the pursuit of knowledge and skills must coexist with other commitments, such as full-time jobs and personal responsibilities. Effectively managing time and developing a structured learning plan is crucial in striking a balance. Tips such as identifying pockets of dedicated learning time, breaking down complex concepts into manageable chunks, and seeking mentorships or online communities can empower individuals to navigate the data science journey while juggling other responsibilities.

Ethical Considerations: Navigating the World of Data Responsibly

As data scientists, we must navigate the world of data responsibly, being mindful of the ethical considerations inherent in this field. Safeguarding privacy, addressing bias in algorithms, and ensuring transparency in data-driven decision-making are critical principles. Exploring topics such as algorithmic fairness, data anonymization techniques, and the societal impact of data science encourages responsible and ethical practices in a rapidly evolving digital landscape.

Embarking on a data science adventure from a curious novice to a passionate data enthusiast is an exhilarating and rewarding journey. By laying a foundation of knowledge, exploring various data science disciplines, acquiring essential skills and tools, and engaging in continuous learning, one can conquer challenges, build a successful career, and have a good influence on the data science community. It's a journey that never truly ends, as data continues to evolve and offer exciting opportunities for discovery and innovation. So, join me in your data science adventure, and let the exploration begin!

[waquasuniverse]

Natural Language Processing Market To Reach $439.85Bn By 2030

The global natural language processing market size is estimated to reach USD 439.85 billion by 2030, expanding at a CAGR of 40.4% from 2023 to 2030, according to a new study by Grand View Research, Inc. Machine learning is predicted to play a critical role in natural language processing (NLP) techniques, mostly in text analytics, as AI advances. In the future, unsupervised and supervised learning…