Top 5 key developments happening today in the AI and tech space.

1. OpenAI raised $6.6 billion, reaching a valuation of $157 billion, highlighting investor interest in generative AI.

2. Nvidia reported record quarterly revenue of $30 billion, with a 154% increase in data center revenue driven by AI demand.

3. New AI coding assistants like Poolside AI ($626M) and Magic ($465M) are enhancing developer productivity through advanced tools.

4. The White House launched a task force to coordinate policies on AI regulation, focusing on economic and environmental concerns.

5. AI adoption is surging across industries, with significant growth seen in healthcare, finance, and customer service sectors.

In Q3 earning call today, Google CEO said more than 25% of Google's new code is generated by AI

AI as a life saver:
1. ChatGPT - thesis, essay, writing
2. Scite and perplexity - literature review
3. Consesus - latest research paper
4. Gemini - coding and technical
5. Claude AI - Analysis data, comparison data, literature review

Neural Networks and Deep Learning
Neural networks and deep learning are integral parts of artificial intelligence (AI) and machine learning (ML). Here's an overview:

1.Neural Networks: Neural networks are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes (neurons) organized in layers: input layer, hidden layers, and output layer.

Each neuron receives input, processes it through an activation function, and passes the output to the next layer. Neurons in subsequent layers perform more complex computations based on previous layers' outputs.

Neural networks learn by adjusting weights and biases associated with connections between neurons through a process called training. This is typically done using optimization techniques like gradient descent and backpropagation.

2.Deep Learning : Deep learning is a subset of ML that uses neural networks with multiple layers (hence the term "deep"), allowing them to learn hierarchical representations of data.

These networks can automatically discover patterns, features, and representations in raw data, making them powerful for tasks like image recognition, natural language processing (NLP), speech recognition, and more.

Deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformer models have demonstrated exceptional performance in various domains.

3.Applications Computer Vision: Object detection, image classification, facial recognition, etc., leveraging CNNs.

Natural Language Processing (NLP) Language translation, sentiment analysis, chatbots, etc., utilizing RNNs, LSTMs, and Transformers.
Speech Recognition: Speech-to-text systems using deep neural networks.

4.Challenges and Advancements: Training deep neural networks often requires large amounts of data and computational resources. Techniques like transfer learning, regularization, and optimization algorithms aim to address these challenges.

LAdvancements in hardware (GPUs, TPUs), algorithms (improved architectures like GANs - Generative Adversarial Networks), and techniques (attention mechanisms) have significantly contributed to the success of deep learning.

5. Frameworks and Libraries: There are various open-source libraries and frameworks (TensorFlow, PyTorch, Keras, etc.) that provide tools and APIs for building, training, and deploying neural networks and deep learning models.

Join for more: https://t.me/machinelearning_deeplearning

How to learn Artificial Intelligence from scratch
👇👇
https://medium.com/@data_analyst/how-to-learn-artificial-intelligence-from-scratch-d34ea18f70c1?sk=b139911f85a0d0c0ecd448a7fffe4c9d

5 Algorithms you must know as a data scientist 👩‍💻 🧑‍💻

1. Dimensionality Reduction
- PCA, t-SNE, LDA

2. Regression models
- Linesr regression, Kernel-based regression models, Lasso Regression, Ridge regression, Elastic-net regression

3. Classification models
- Binary classification- Logistic regression, SVM
- Multiclass classification- One versus one, one versus many
- Multilabel classification

4. Clustering models
- K Means clustering, Hierarchical clustering, DBSCAN, BIRCH models

5. Decision tree based models
- CART model, ensemble models(XGBoost, LightGBM, CatBoost)

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://t.me/free4unow_backup

Like if you need similar content 😄👍

Introducing ChatGPT search

ChatGPT can now search the web in a much better way than before. You can get fast, timely answers with links to relevant web sources, which you would have previously needed to go to a search engine for. This blends the benefits of a natural language interface with the value of up-to-date sports scores, news, stock quotes, and more.

ChatGPT will choose to search the web based on what you ask, or you can manually choose to search by clicking the web search icon.
On mobile, the option will replace the existing “Refine my draft” shortcut. Instead of swiping to see options to polish.

Search will be available at chatgpt.com⁠(opens in a new window), as well as on our desktop and mobile apps. All ChatGPT Plus and Team users, as well as SearchGPT waitlist users, will have access today. Enterprise and Edu users will get access in the next few weeks. We’ll roll out to all Free users over the coming months.

85 career-focused Data and AI courses for FREE until Nov 21st
👇👇
https://365datascience.pxf.io/BnE1P4

No credit card required

Google could add AI replies to its handy call-screening feature

Google could soon add “AI Replies” to the Phone app’s call-screening feature. A line of code spotted by 9to5Google suggests the app will generate “new AI-powered smart replies” based on how someone responds to the call screen.

Google widely rolled out its call-screening feature in Android 12. It allows you to filter calls and have Google Assistant respond with an audio message to ask who’s calling, rather than having to pick up the call yourself. Late last year, Google added “contextual replies,” which use the context of someone’s call to serve up customized audio responses. It also updated its call-screening feature in March with a way to respond even when the caller is silent.


Source-Link: The Verge

Time to have an uncomfortable conversation 😬

📊 67.6% of developers admire Python. 58.3% admire JavaScript. That's almost a 10-point difference in favor of Python.

💡39.8% want to learn JavaScript, but 41.9% want to learn Python. That's a 2+ difference.

🚀 TypeScript doesn't do better. Only 33.8% want to learn TypeScript, although it's more admired than Python, with 69.5%. 🤷‍♂️

📖 This data is from the 2024 Stack Overflow Survey. 📋

🌍 On top of that, Python has surpassed JavaScript as the most popular programming language on GitHub this year.

There's a clear trend here. 📈

This is the first chapter of what will become a complete Python dominance (likely thanks to the rise of AI). 🤖✨

Machine Learning Model

This message is for all those people looking for new opportunities or learning new skills thinking if they'll earn more, sustain in this life or not.

AI will take the job.
Will there be new opportunities in 2024.
How many days will it take to learn this skill.
Why I am still not successful.

I am sharing some bit of experience with you all based on whatever I observed in this world.

Don't think too much. Everything takes some time.

Rather just focus on your goal and do something which keep you closer to that. Stay consistent & work on something that your future self will be proud of.

There will be some days when you'll find yourself doing nothing. But just ignore it and learn from the failures without thinking anything negative.

In case I can be of any help to you, feel free to reach out to me either through Instagram or Telegram.

Never stop learning ❤️

Learning can be anything - new skill or habit. So just enjoy the process even if it takes time.

ENJOY LEARNING 👍👍

15 ChatGPT Prompts for Your Career Growth!

With These Game-Changing ChatGPT Prompts
You Can Explore Your⤵️

1. Career Path Exploration
2. Job Responsibilities Insight
3. Pros and Cons Analysis
4. Emerging Fields Discovery
5. Skills Demand Overview

6. Career Path Comparison
7. Career Transition Steps
8. Growth Opportunities Exploration
9. Job Market Trends Insights
10. Alternative Career Paths

11. Skill Set Requirements
12. Lesser-Known Career Opportunities
13. Education and Experience Leverage
14. Work Environment Overview
15. Personality-Based Career Suggestions

Read it here: https://t.me/aiindi/226

Top 10 machine Learning algorithms 👇👇

1. Linear Regression: Linear regression is a simple and commonly used algorithm for predicting a continuous target variable based on one or more input features. It assumes a linear relationship between the input variables and the output.

2. Logistic Regression: Logistic regression is used for binary classification problems where the target variable has two classes. It estimates the probability that a given input belongs to a particular class.

3. Decision Trees: Decision trees are a popular algorithm for both classification and regression tasks. They partition the feature space into regions based on the input variables and make predictions by following a tree-like structure.

4. Random Forest: Random forest is an ensemble learning method that combines multiple decision trees to improve prediction accuracy. It reduces overfitting and provides robust predictions by averaging the results of individual trees.

5. Support Vector Machines (SVM): SVM is a powerful algorithm for both classification and regression tasks. It finds the optimal hyperplane that separates different classes in the feature space, maximizing the margin between classes.

6. K-Nearest Neighbors (KNN): KNN is a simple and intuitive algorithm for classification and regression tasks. It makes predictions based on the similarity of input data points to their k nearest neighbors in the training set.

7. Naive Bayes: Naive Bayes is a probabilistic algorithm based on Bayes' theorem that is commonly used for classification tasks. It assumes that the features are conditionally independent given the class label.

8. Neural Networks: Neural networks are a versatile and powerful class of algorithms inspired by the human brain. They consist of interconnected layers of neurons that learn complex patterns in the data through training.

9. Gradient Boosting Machines (GBM): GBM is an ensemble learning method that builds a series of weak learners sequentially to improve prediction accuracy. It combines multiple decision trees in a boosting framework to minimize prediction errors.

10. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving as much variance as possible. It helps in visualizing and understanding the underlying structure of the data.

Credits: https://t.me/datasciencefun

Like if you need similar content 😄👍

Hope this helps you 😊

👨‍💻 Python course from Harvard University!

✅ A large playlist with a cool explanation of the language, perhaps one of the best courses on Python!

🔗 Link: https://www.youtube.com/playlist?list=PLhQjrBD2T3817j24-GogXmWqO5Q5vYy0V

#python