Machine Learning with Python Telegram Channel Link

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🔖 A Comprehensive Resource for Machine Learning PracticeThe platform offers:🫡 1200+ machine learning tasks;🫡 Mathematics, from linear algebra to PCA;🫡 Deep Learning, NLP, and Computer Vision;🫡 Labs with real datasets;🫡 Learning paths and interview preparation;🫡 Projects using GPT, RL, and CUDA.Almost everything can be accessed directly in your browser, and basic tasks are available for free.⛓️ Link to the platformhttps://enghusseinsheikho.substack.com/p/a-comprehensive-resource-for-machine?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true#MachineLearning #DeepLearning #DataScience #AI #Coding #Tech✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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"Graph Theory with Applications" is a free textbook that provides a comprehensive introduction to graph theory and its practical applications.The book begins with basic concepts: graphs, subgraphs, paths, cycles, trees, and connectivity. It then moves on to more advanced topics: Eulerian and Hamiltonian graphs, matching, graph coloring, planar and directed graphs, network flows, and extremal graph theory.What is particularly useful is that the authors combine a visual, geometric, and topological understanding of graphs with a rigorous presentation of the mathematical foundations.This makes the book a valuable resource for studying the theory that underlies many modern applications of graphs in computer science, computer networks, optimization, data analysis, and related fields.Book: https://book.huihoo.com/pdf/graph-theory-with-applications.pdfhttps://t.me/CodeProgrammer

📚 Download 10 Python books for FREE:1. Think Python — O’Reillyhttps://t.co/X7y3pX68IW2. The Big Book of Small Python Projectshttps://inventwithpython.com/bigbookpython/3. Data Structureshttps://opendatastructures.org/ods-python.pdf4. Data Science Handbookhttps://t.co/9aOLdiAbim5. Data Analysishttps://wesmckinney.com/book/6. Data Sciencehttps://t.co/TusyBWGfrx7. Machine Learninghttps://t.co/7veOwESe8q8. Statisticshttps://t.co/waWDQylQpY9. Think Bayeshttps://allendowney.github.io/ThinkBayes2/10. Clean Code in Python — Packthttps://t.co/OHhVgF1L65#Python #FreeBooks #Programming #DataScience #MachineLearning #Coding✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

One of the key moments when I truly understood how transformers work: 🧠✨"Stop thinking of a transformer as a conveyor belt, where each layer transforms the output of the previous one.""Start thinking of it as a residual flow." 🌊Each block in a transformer has a residual connection that adds the input of the block to its output:x' = f(x) + xBecause of this addition, the attention mechanism or MLP within the layer – the function f in the formula above – actually does NOT transform the input. Instead, it calculates the information that needs to be ADDED to the input before passing it to the next block! ➕Imagine the main part of the transformer as a shared whiteboard. 📝 Each block reads what it needs from it and adds its own notes. All changes are additions.Furthermore, layers can exchange information over distances. A block in the first layer can write information, and a block in the fifth layer can read it, even though there is no direct connection between them. 🔗I owe these ideas to an older article by Anthropic about the architecture of transformers:transformer-circuits.pub/2021/framework#Transformers #DeepLearning #AI #MachineLearning #NeuralNetworks #Tech✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

🔖 Official PyTorch TutorialsThis collection includes:🫡 PyTorch fundamentals and torch.nn;🫡 Computer Vision and transfer learning;🫡 NLP and RNNs;🫡 Reinforcement Learning;🫡torch.compileand ONNX;🫡 Distributed Training, FSDP, and Tensor Parallel;🫡 Profiling and memory optimization;🫡 Model serving and Ray.A useful resource for both beginners and those already working with PyTorch in production.⛓https://docs.pytorch.org/tutorials/

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Understanding algorithms changes the way you write code. 🧠 This playlist teaches you exactly that: understanding.Pavel Mavrin. 61 lectures. Free. 🎓Here's the difference:Before understanding algorithms:- You use hash tables for everything.- You write the code first, then optimize it.- You estimate the time complexity randomly.After:- You choose the appropriate data structure for the task.- You think about complexity before writing the code.- You learn an approach that is suitable for solving the problem.What you will learn:→ Time complexity and sorting.→ Dynamic programming.→ Advanced trees: segment tree, Fenwick tree, splay tree, link-cut tree.→ Graph algorithms: traversals, shortest paths, flows.→ String algorithms: substring search, suffix structures.→ Fast Fourier Transform, linear programming, cryptography.https://www.youtube.com/playlist?list=PLrS21S1jm43igE57Ye_edwds_iL7ZOAG4https://t.me/CodeProgrammer#Algorithms #ComputerScience #Programming #DataStructures #Learning #Coding✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

I found a great resource for interactive learning about machine learning and AI – VizLearn.You can experiment with gradient descent, SVM, PCA, the Bayesian method, BPE tokenization, Q/K/V, KV-cache, quantization, and much more. You can change the input data and see how the calculations themselves change.It's free and doesn't require registration.https://vizlearn.inhttps://t.me/MachineLearning9

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"How to Train a Neural Network" is a concise summary of the MIT course lectures on deep learning from 2024. It focuses on one of the fundamental questions in neural networks: how a model learns its weights.The summary examines the training process from a mathematical perspective. It covers topics such as forward propagation, loss functions, gradients, backpropagation, and gradient-based optimization methods.I believe this is an interesting resource for those who want to go beyond a general, intuitive understanding of neural networks and begin to delve into the mathematics that underlies their training.https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/mit6_7960_f24_lec2.pdfhttps://t.me/CodeProgrammer 🤩

A collection of explanations for key concepts and scientific papers in machine learning:https://github.com/dair-ai/ML-Papers-Explained

Machine Learning with Python pinned a video

🧲 Your agent writes the tool. You keep the terminal closed.You know the shape of the script before you open the editor. The hour goes to argparse, a retry wrapper, a rate limiter you have written eleven times already.Create your own AI agent inside Telegram in about a minute, and create small tools with it right in the chat.▫️ describe a tool in a sentence and it writes, runs and returns the working script▫️ ships a mini-app inside Telegram — a form, a converter, a dashboard, no deploy and no hosting▫️ drop in a traceback or a repo link and get the fix, not a lecture▫️ swap the model per task with one command, so cheap work runs cheap▫️ remembers your stack, your conventions and your project for months▫️ voice in, answer back — describe the task on the way home, read the result when you are backSetup takes a minute: open the link and name your agent.Free to use — no card needed.🧲 Try your own agent

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