#AI/ML technologies

[anna-scribbles]

i think it would be funny if one of the ways that adrien responded to Everything was by becoming the most offline unreachable hermit of all time. no phone no computer nothing. don’t talk to him about ai or instagram brand deals or twitter drama he can’t even receive a text message. if you want to contact him he checks his email once a week at the local library. he also has a flip phone with customized ringtones and no internet connection, and a number he gives out to an extremely select list of people. don’t try to reference a pop culture phenomenon to him he won’t understand it. his flip phone can’t open tiktoks. he’s protecting his peace.

[queen-mabs-revenge]

I'm just saying maybe you shouldn't talk about the Luddites if you actually refuse to engage with their historical struggle and the reasons for it and instead just perpetuate the bourgeois propaganda about them and somehow act that's the materialist take. get fucking serious please.

[noahboswel]

Great Article By Ashkan Rajaee

[giovannivi95552]

Great Article By Ashkan Rajaee

[fraoula1]

Python for Data Science: From Beginner to Expert – A Complete Guide!

Python has become the go-to language for data science, thanks to its flexibility, powerful libraries, and strong community support. In this video, we’ll explore why Python is the best choice for data scientists and how you can master it—from setting up your environment to advanced machine learning techniques.

🔹 What You'll Learn:

✅ Why Python is essential for data science

✅ Setting up Python and key libraries (NumPy, Pandas, Matplotlib) ✅ Data wrangling, visualization, and transformation

✅ Building machine learning models with Scikit-learn

✅ Best practices to enhance your data science workflow 🚀 Whether you're a beginner or looking to refine your skills, this guide will help you level up in data science with Python. 📌 Don’t forget to like, subscribe, and hit the notification bell for more data science and Python content!

[hawkeyedaniel]

Great Article By Ashkan Rajaee

[khushidubeyblog]

PGDM Specialization in AI & ML: Preparing for the Future of Business and Technology

[ogxfuturetech]

The Comprehensive Guide to Web Development, Data Management, and More 

Introduction 

Everything today is technology driven in this digital world. There's a lot happening behind the scenes when you use your favorite apps, go to websites, and do other things with all of those zeroes and ones — or binary data. In this blog, I will be explaining what all these terminologies really means and other basics of web development, data management etc. We will be discussing them in the simplest way so that this becomes easy to understand for beginners or people who are even remotely interested about technology.  JOIN US

What is Web Development? 

Web development refers to the work and process of developing a website or web application that can run in a web browser. From laying out individual web page designs before we ever start coding, to how the layout will be implemented through HTML/CSS. There are two major fields of web development — front-end and back-end. 

Front-End Development 

Front-end development, also known as client-side development, is the part of web development that deals with what users see and interact with on their screens. It involves using languages like HTML, CSS, and JavaScript to create the visual elements of a website, such as buttons, forms, and images. JOIN US

HTML (HyperText Markup Language): 

HTML is the foundation of all website, it helps one to organize their content on web platform. It provides the default style to basic elements such as headings, paragraphs and links. 

CSS (Cascading Style Sheets):  

styles and formats HTML elements. It makes an attractive and user-friendly look of webpage as it controls the colors, fonts, layout. 

JavaScript :  

A language for adding interactivity to a website Users interact with items, like clicking a button to send in a form or viewing images within the slideshow. JOIN US

Back-End Development 

The difference while front-end development is all about what the user sees, back end involves everything that happens behind. The back-end consists of a server, database and application logic that runs on the web. 

Server: 

A server is a computer that holds website files and provides them to the user browser when they request it. Server-Side: These are populated by back-end developers who build and maintain servers using languages like Python, PHP or Ruby. 

Database:  

The place where a website keeps its data, from user details to content and settings The database is maintained with services like MySQL, PostgreSQL, or MongoDB. JOIN US

Application Logic —  

the code that links front-end and back-end It takes user input, gets data from the database and returns right informations to front-end area. 

Why Proper Data Management is Absolutely Critical 

Data management — Besides web development this is the most important a part of our Digital World. What Is Data Management? It includes practices, policies and procedures that are used to collect store secure data in controlled way. 

Data Storage –  

data after being collected needs to be stored securely such data can be stored in relational databases or cloud storage solutions. The most important aspect here is that the data should never be accessed by an unauthorized source or breached. JOIN US

Data processing:  

Right from storing the data, with Big Data you further move on to process it in order to make sense out of hordes of raw information. This includes cleansing the data (removing errors or redundancies), finding patterns among it, and producing ideas that could be useful for decision-making. 

Data Security:  

Another important part of data management is the security of it. It refers to defending data against unauthorized access, breaches or other potential vulnerabilities. You can do this with some basic security methods, mostly encryption and access controls as well as regular auditing of your systems. 

Other Critical Tech Landmarks 

There are a lot of disciplines in the tech world that go beyond web development and data management. Here are a few of them: 

Cloud Computing 

Leading by example, AWS had established cloud computing as the on-demand delivery of IT resources and applications via web services/Internet over a decade considering all layers to make it easy from servers up to top most layer. This will enable organizations to consume technology resources in the form of pay-as-you-go model without having to purchase, own and feed that infrastructure. JOIN US

Cloud Computing Advantages:  

Main advantages are cost savings, scalability, flexibility and disaster recovery. Resources can be scaled based on usage, which means companies only pay for what they are using and have the data backed up in case of an emergency. 

Examples of Cloud Services: 

Few popular cloud services are Amazon Web Services (AWS), Microsoft Azure, and Google Cloud. These provide a plethora of services that helps to Develop and Manage App, Store Data etc. 

Cybersecurity 

As the world continues to rely more heavily on digital technologies, cybersecurity has never been a bigger issue. Protecting computer systems, networks and data from cyber attacks is called Cyber security. 

Phishing attacks, Malware, Ransomware and Data breaches: 

This is common cybersecurity threats. These threats can bear substantial ramifications, from financial damages to reputation harm for any corporation. 

Cybersecurity Best Practices:  

In order to safeguard against cybersecurity threats, it is necessary to follow best-practices including using strong passwords and two-factor authorization, updating software as required, training employees on security risks. 

Artificial Intelligence and Machine Learning 

Artificial Intelligence (AI) and Machine Learning (ML) represent the fastest-growing fields of creating systems that learn from data, identifying patterns in them. These are applied to several use-cases like self driving cars, personalization in Netflix. 

AI vs ML —  

AI is the broader concept of machines being able to carry out tasks in a way we would consider “smart”. Machine learning is a type of Artificial Intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. JOIN US

Applications of Artificial Intelligence and Machine Learning: some common applications include Image recognition, Speech to text, Natural language processing, Predictive analytics Robotics. 

Web Development meets Data Management etc. 

We need so many things like web development, data management and cloud computing plus cybersecurity etc.. but some of them are most important aspects i.e. AI/ML yet more fascinating is where these fields converge or play off each other. 

Web Development and Data Management 

Web Development and Data Management goes hand in hand. The large number of websites and web-based applications in the world generate enormous amounts of data — from user interactions, to transaction records. Being able to manage this data is key in providing a fantastic user experience and enabling you to make decisions based on the right kind of information. 

E.g. E-commerce Website, products data need to be saved on server also customers data should save in a database loosely coupled with orders and payments. This data is necessary for customization of the shopping experience as well as inventory management and fraud prevention. 

Cloud Computing and Web Development 

The development of the web has been revolutionized by cloud computing which gives developers a way to allocate, deploy and scale applications more or less without service friction. Developers now can host applications and data in cloud services instead of investing for physical servers. 

E.g. A start-up company can use cloud services to roll out the web application globally in order for all users worldwide could browse it without waiting due unavailability of geolocation prohibited access. 

The Future of Cybersecurity and Data Management 

Which makes Cybersecurity a very important part of the Data management. The more data collected and stored by an organization, the greater a target it becomes for cyber threats. It is important to secure this data using robust cybersecurity measures, so that sensitive information remains intact and customer trust does not weaken. JOIN US

Ex: A healthcare provider would have to protect patient data in order to be compliant with regulations such as HIPAA (Health Insurance Portability and Accountability Act) that is also responsible for ensuring a degree of confidentiality between a provider and their patients. 

Conclusion 

Well, in a nutshell web-developer or Data manager etc are some of the integral parts for digital world.

As a Business Owner, Tech Enthusiast or even if you are just planning to make your Career in tech — it is important that you understand these. With the progress of technology never slowing down, these intersections are perhaps only going to come together more strongly and develop into cornerstones that define how we live in a digital world tomorrow. 

With the fundamental knowledge of web development, data management, automation and ML you will manage to catch up with digital movements. Whether you have a site to build, ideas data to manage or simply interested in what’s hot these days, skills and knowledge around the above will stand good for changing tech world. JOIN US

[simple-logic]

Which is a command to create a new directory? 🗂️

a) mkdir 🖥️

b) mkdir -p 🛠️

c) create dir 🚫

d) newdir 🚪

📂 Time for a tech challenge!

Comment your answer below

[grey-space-computing]

Deliver personalized user experiences with machine learning in your app. Understand your users better and give them exactly what they need. 🔗Learn more: https://greyspacecomputing.com/custom-mobile-application-development-services/  📧 Visit: https://greyspacecomputing.com/portfolio

[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.

[unforth]

Y'all I know that when so-called AI generates ridiculous results it's hilarious and I find it as funny as the next guy but I NEED y'all to remember that every single time an AI answer is generated it uses 5x as much energy as a conventional websearch and burns through 10 ml of water. FOR EVERY ANSWER. Each big llm is equal to 300,000 kiligrams of carbon dioxide emissions.

LLMs are killing the environment, and when we generate answers for the lolz we're still contributing to it.

Stop using it. Stop using it for a.n.y.t.h.i.n.g. We need to kill it.

Sources:

[mooglelabs]

How Generative AI is Powering the No-Code Development Revolution

Generative AI has revolutionized app creation by removing the need for coding expertise. Consequently, software development is no longer an exclusive domain of engineers.

Now business analysts, marketers educators and entrepreneurs– basically anyone with an idea– can also bring those ideas to life in the form of working applications!

But what makes these technologies so effective? Let's explore some key factors:

Simplified drag-and-drop interfaces: Visual builders have made coding obsolete; you don't need to write code anymore.

AI integration: Tools such as Bubble. io and Zoho Creator can propose UI components, logic flows, and even content tailored to your requirements.

Real-time prototyping: You can now build, test, and refine your concepts quickly— without waiting in a lengthy development queue.

Workflow automation: Hassle-free integration is possible thanks to pre-built connectors— for instance linking up with CRM systems, email databases plus so much more! Also, AI can offer useful suggestions wherever needed.

Faster time-to-market: Launch your minimum viable products within days rather than dragging it out over months on end.

Lower costs: Forget the expense of hiring large development teams just for preliminary stages.

Better collaboration: People without a technical background can now have meaningful input into application development processes.

Scalability: Numerous no-code platforms provide paid options designed explicitly for scaling at the enterprise level.

If you're pressed for time, money, or developers, then no-code/low-code development services enhanced with Generative AI might be just what you need– building fast affordable scalable digital solutions has never been easier!

Why not give it a go? Start exploring tools like Zoho Creator Replit AI and Bubble today. Unlock innovation regardless of whether you have any prior coding experience!

Click here to read full blog: https://www.mooglelabs.com/blog/generative-ai-services-with-low-code-no-code-development

[jrnam]

Why Mobile App Development Complements Web Development

One major shift I’ve seen is businesses extending their web strategy into mobile app development. And it makes total sense.

Think about how often you use your phone every day. Now think about your customers doing the same. If your business has a mobile app, you’re suddenly in their pocket—literally.

Mobile apps:

Offer faster access and better performance than websites

Enable push notifications and offline access

Build deeper engagement with users

Use device features like GPS, camera, or biometrics

Create more personalized experiences

The best part? Your app and website don’t have to be separate projects. When built strategically, they complement each other—offering a seamless experience across platforms.

[charlenthetical]

Machine Learning models are incapable of producing anything new. Everything that they produce is based upon mathematics and procedures dictated to them by a programmer over which they iterate over and over again.

While the results may appear novel or otherwise original, every pixel is based upon the datasets from which the model was trained and the functions provided to manipulate that data. It is not inspiration; it is dutifully-followed instruction.

While the models may be used in creative efforts, it's important to realize and remember that, unless you trained the model yourself, there is no way that you will know from what the work was generated. And, even if you did train it yourself, why would you use the work of others without their permission to do so? Would you similarly plagiarize them were it not automatic?

When a human uses inspiration or attempts to duplicate another's work, there is always the element of chaos that comes from being an organism; it is impossible for anything to recreate that. These models can only produce a facsimile — and, in artistic expression, this means everything.

Behind every artistic endeavour are thoughts, emotions, memories. This is not the same case for generated art. And it is fine if you believe this to be unimportant to the final result. You're wrong to think so, but I won't challenge you on the importance. It's still plagiarism, which we have agreed, as a society, is an issue.

Machine learning is useful. I've created my own models and algorithms, and I find it intensely fascinating. It is a great tool that will unlock many possibilities. But art is not the field that it can or should be applied to.

Your art is a gift to the world; don't discount the impact it has. Don't rob the world of what only you can give. And don't rob others of their chance to give what only they can.

so a huge list of artists that was used to train midjourney’s model got leaked and i’m on it

literally there is no reason to support AI generators, they can’t ethically exist. my art has been used to train every single major one without consent lmfao 🤪

link to the archive