#Deep Learning solutions

[its-vishnu-stuff]

Deep Learning Services In Hyderabad– Innodatatics

Deep Learning Analytics Services provide state-of-the-art ways to leverage machine learning and artificial intelligence. These services offer thorough data analysis, predictive modeling, and actionable insights to support well-informed decision-making and operational optimization by utilizing cutting-edge neural networks and complex algorithms.

[faejilly]

i think the fandom doesn’t capitalize on the fact that Alec is a older brother, that man probably has so much more black mail on Izzy and Jace then they do on him

#mood

Izzy and Jace have nothing they can use on Alec.

One: They didn't think they'd ever need to because they thought they knew what he'd do in most situations (say no and then begrudgingly help them anyway, and tbf to them, they're mostly not wrong pre-Magnus)

Two: He is so aggressively self-contained for survival reasons that the only 'secrets' either of them has on him are the facts that he's gay and he and his mother have a fucked up relationship and THOSE ARE NOT THINGS YOU CAN USE FOR 'friendly family blackmail'.

Three: Alec is a tactician, he is observant, he is ruthless. He knows everything Izzy and Jace have ever done and the fact that he (as far as we know) never uses any of it is amazing and a little heart-breaking because he could have manipulated and threatened the fuck out of them and made his own life so much easier and he never ever did.

[drachenengel]

[bahadurislam011444]

Unveiling the Best SEO Worker in Bangladesh: Driving Digital Success

[worthyourweightinfanfiction]

Student schedule was the fucking best. I’d walk max. 10 mins away from my house. Get my mind blown open by smart people for an hour. Fuck around for the rest of the day letting my mind chew over what I learned. Walk into town if I feel like it go to H&M if I feel like it go to a cafe if I feel like it. Get fresh bread and eat it on the walk home if I fucking feel like it dude. Get home there are people my age with free time in my house, spend hours getting ready for a night out, immediately abandon them and have fun dancing to shit music. God damn I miss that.

[elibean]

oh no, the bane of every translator’s existence. 

so for those of you who don’t know, shiritori is a very simple japanese game. you say a word, and then take the last “letter” (in japanese it’s actually closer to a sound I guess) of that word to make a new word. if you choose a word that ends in “ん”, or “n”, you lose, because no word in Japanese starts with ん. 

this is a very popular way to introduce word play or just fill in the space or whatever in anime and manga, but sometimes authors get REALLY creative with it and it can be a nightmare because of course these words don’t map 1-to-1 with English. anyway, let’s go through it!

so the “k” is “ノリ”, “nori”. you may have heard the word nori before, but it’s usually translated as “seaweed”. in order to make it work for the next line the translator instead went with “kelp”. this is actually kind of interesting, listening back to it. in the anime, black-hair says “nori”, and then blondie works off of “ri” to get to the next word. the next word ends in “ku” so then red-head works off of “ku”. but the subs competley skip over the two words here and instead include “nori/kelp” for two lines, haha. basically, “let’s start with k!” “k...k...kelp” is just one word, while the Japanese actually has two words here (both “nori” and the next word).

ANYWAY. the word after “nori” is “riku” which I actually am unfamiliar with. because I don’t have kanji to work off of, I can’t be 100% sure, but my best guess is “陸”, which google is telling me can also be “shore”. the English completely skips this (like I mentioned above) in order to connect with the next joke.

red-head works off of “ku” from “riku” and comes up with “空気入れ忘れた!” “空気入れ” is “air pump”. it ends in “re”, so the next character works off of that. 

he says “レモンチョウチョウウオ”, which, after looking for that a lot longer than I’d like to admit, I came up with “dotted butterfly fish”. it’s FOUND in the Pacific Ocean (and Indian I think), but I couldn’t find any reference to that being used in its name. but for the sake of keeping the word train alive, ya do what ya gotta do I guess xD

I cut it off here, but in the next few lines there is a direct visual reference to the fish in question, so the translator is trapped into making the wordplay end with SOME kind of reference to a fish-- if there was no visual reference they might have been able to make it work a bit differently, or avoid having to skip the “riku” word. 

[imabiscuitinthousandworlds]

welcome to a new episode of "crisis", today about researching inattentive adhd and trying to find out whether you're being rational or trying to find excuses-

[narolainfotechus]

Narola Infotech is the #1 machine learning development company in the USA. With a 100% project delivery rate, our machine learning solutions development has helped clients build successful businesses.

[jcmarchi]

Business readiness for the impending deepfake superstorm - CyberTalk

New Post has been published on https://thedigitalinsider.com/business-readiness-for-the-impending-deepfake-superstorm-cybertalk/

Business readiness for the impending deepfake superstorm - CyberTalk

EXECUTIVE SUMMARY:

Deepfake technologies, as powered by artificial intelligence (AI), are rapidly proliferating, affecting businesses both large and small, worldwide. Between last year and this year, AI-driven deepfake attacks have increased by an astonishing 3,000%. Although deepfake technologies do have legitimate applications, the risk that they pose to businesses is non-trivial. The following serves as a brief overview of what to keep track of:

Business risk

1. Deepfakes impersonating executives. At this point, deepfakes can mimic the voices and appearances of high-ranking individuals so effectively that cyber criminals are manipulating financial transactions, ensuring authorization of payments, and weaponizing videos to gain access to information. The financial losses caused by deepfakes can prove substantial. Think $25 million or more, as exemplified in this incident. Millions of dollars lost can affect the company’s gross revenue, jeopardizing a company’s future.

What’s more is that impersonation of an executive, even if it only occurs once, can send stakeholders into a tailspin as they wonder who to trust, when to trust them and whether or not to only trust people in-person. This can disrupt the fluidity of day-to-day operations, causing internal instability and turmoil.

2. Reputational damage. If deepfakes are used publicly against an organization – for example, if a CEO is shown to be on stage, sharing a falsehood – the business’s image may rapidly deteriorate.

The situation could unravel further in the event that a high-level individual is depicted as participating in unethical or illegal behavior. Rebuilding trust and credibility after such incidents can be challenging and time-consuming (or all-out impossible).

3. Erosion of public trust. Deepfakes can potentially deceive customers, clients and partners.

For example, a cyber criminal could deepfake a customer service representative, and could pretend to assist a client, stealing personal details in the process. Or, a partner organization could be misled by deepfake impersonators on a video call.

These types of events can erode trust, lead to lost business and result in public reputational harm. When clients or partners report deepfake issues, news headlines emerge quickly, and prospective clients or partners are liable to back out of otherwise value-add deals.

Credit risk warning

Cyber security experts aren’t the only people who are concerned about the impending “deepfake superstorm” that threatens to imperil businesses. In May, credit ratings firm Moody’s warned that deepfakes could pose credit risks. The corresponding report points to a series of deepfake scams that have impacted the financial sector.

These scams have frequently involved fake video calls. Preventing deepfake scams – as through stronger cyber security and related measures – can potentially present businesses with greater opportunities to ensure good credit, acquire new capital and obtain lower insurance rates, among other things.

Cyber security solutions

Deepfake detection tools can help. Such tools typically use a variety of deepfake identification techniques to prevent and mitigate the threats. These include deep learning algorithms, machine learning models and more.

Check Point Research (CPR) actively investigates emerging threats, including deepfakes, and the research informs Check Point’s robust security solutions, which are designed to combat deepfake-related risks.

To see how a Check Point expert views and prevents deepfakes, please see CyberTalk.org’s past coverage. Lastly, to receive cyber security thought leadership articles, groundbreaking research and emerging threat analyses each week, subscribe to the CyberTalk.org newsletter.

[its-vishnu-stuff]

Top Deep Learning Services– Innodatatics

Using sophisticated neural networks, our Deep Learning Analytics services provide innovative insights and solutions specifically tailored to your company's requirements. We convert enormous volumes of data into actionable insights by utilizing cutting-edge machine learning algorithms, giving you the ability to make decisions with unprecedented accuracy.

[mobiloitteinc02]

Artificial Intelligence Software Development Company- Mobiloitte USA

Mobiloitte is a leading artificial intelligence (AI) solutions company in the USA. We offer a wide range of AI services to help businesses of all sizes achieve their goals.

Our team of experienced and skilled AI experts can help you with every aspect of your AI journey, from ideation and design to development, testing, and deployment.

We have a proven track record of success in helping businesses of all sizes implement and leverage AI to improve their operations, increase efficiency, and boost profits.

[aitoolsa2z]

18 AI-powered cybersecurity and fraud detection tools along with precautions you can take to protect yourself. Each tool has unique features, advantages, and considerations. Remember that staying informed and vigilant is crucial in the ever-evolving landscape of online threats.

[zapperrr]

Harnessing the Power of Artificial Intelligence in Web Development

[aibyrdidini]

A DEEP LEARNING MODEL TO FIDELIZATION

Developing a deep learning model for customer loyalty, or fidelization, can be a powerful tool for businesses to retain customers and increase revenue. In this article, we will discuss how to develop a deep learning model for fidelization using Python.

First, we need to gather data on customer behavior and preferences. This can include data on customer purchases, interactions with the business, and feedback. Once we have this data, we can use it to train a deep learning model to predict which customers are most likely to be loyal to the business.

To do this, we can use a variety of deep learning algorithms, such as neural networks or recurrent neural networks. These algorithms can learn to identify patterns in the data that are indicative of customer loyalty.

Once we have trained our model, we can use it to predict which customers are most likely to be loyal to the business. This can help us to identify which customers to target with loyalty programs and other incentives.

Here is a snippet of code in Python that demonstrates how to train a deep learning model for fidelization:

import pandas as pd

import numpy as np

from sklearn.model_selection import train_test_split

from keras.models import Sequential

from keras.layers import Dense

# Load data

data = pd.read_csv('customer_data.csv')

# Split data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(data.drop('loyalty', axis=1), data['loyalty'], test_size=0.2)

# Define model

model = Sequential()

model.add(Dense(128, activation='relu', input_shape=(X_train.shape[1],)))

model.add(Dense(64, activation='relu'))

model.add(Dense(1, activation='sigmoid'))

# Compile model

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Train model

model.fit(X_train, y_train, epochs=10, batch_size=32)

# Evaluate model

score = model.evaluate(X_test, y_test, verbose=0)

print('Test loss:', score[0])

Continue

Once we have trained our deep learning model for fidelization, we can use it to predict which customers are most likely to be loyal to the business. This can help us to identify which customers to target with loyalty programs and other incentives.

For example, we can use the model to predict the loyalty of new customers, and then use this information to tailor our marketing efforts to these customers. We can also use the model to identify which customers are at risk of churning and then take steps to retain them.

Overall, developing a deep learning model for fidelization can be a powerful tool for businesses to retain customers and increase revenue. By using deep learning algorithms to identify patterns in customer behavior and preferences, we can predict which customers are most likely to be loyal to the business, and then use this information to tailor our marketing efforts to these customers.

RDIDINI PROMPT ENGINEER

[kpissolution]

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𝐀𝐈 𝐀𝐏𝐏 𝐃𝐄𝐕𝐄𝐋𝐎𝐏𝐌𝐄𝐍𝐓 𝐒𝐄𝐑𝐕𝐈𝐂𝐄𝐒 are valuable in different industries, including #healthcare, #finance, #ecommerce, #manufacturing, transport, #customerservice, #marketing, #cybersecurity, and many more. With AI-driven applications, businesses can streamline operations, enhance productivity, and provoke growth.

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