Machine Learning with Python

Reinforcement Learning Methods and Tutorials In these tutorials for reinforcement learning, it covers from the basic RL algorithms to advanced algorithms developed recent years. Learning Resources: https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow Here's a collection of simple materials on methods and practical guides, covering both basic reinforcement learning algorithms and modern, recently developed, and updated advanced algorithms.

🚀 Looking for a portfolio-ready NLP project? I recently published an end-to-end walkthrough on Towards Data Science using Kaggle’s Spooky Author Identification dataset. You’ll see how far classical NLP can go with: 📝 Bag-of-Words and TF-IDF 🔤 Character n-grams 📊 Model comparison 🧩 Ensemble stacking It’s a practical project for anyone preparing for an ML/DS role, with no deep learning required. I walk through the entire workflow step by step: 🔗 https://towardsdatascience.com/how-far-can-classical-nlp-go-from-bag-of-words-to-stacking-on-spooky-author-identification/

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🔖 One of the most useful books on Agentic AI This is not just a textbook, but a comprehensive overview of modern LLMs, model training, RL, inference, quality assessment, and building AI agents. It's an excellent option to get a holistic picture and understand which topics deserve deeper study. ⛓️ Link to the book https://arxiv.org/abs/2606.24937

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Understanding Datasets 😉

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Transformers become more understandable when you can "poke" the model directly. 🧠✨ Transformer Explainer is an interactive visualization tool for studying how text-generating transformer-based models, such as GPT, work. 🔍 It helps connect the architecture with real behavior by running a live GPT-2 directly in the browser, allowing you to enter your own text and showing how the internal components work together to predict the next tokens. 🔄📝 Key features: 🌟 - Live GPT-2 in the browser - experiment without setting up a separate model server 💻 - Your own text - try your own prompts and see how the model processes them ✍️ - Internal components - observe the operations working inside the transformer 🔧 - Focus on predicting the next token - link each visual step to the model's predictions 🎯 - Local development - clone the repository, install dependencies, and run via npm for in-depth study ⚙️ It's open-source (MIT license). 📜 https://github.com/poloclub/transformer-explainer #AI #MachineLearning #GPT #DataScience #TechTools #OpenSource ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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