Machine Learning with Python

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📰 Awesome Open Source AI 2026 — A comprehensive collection of current open-source AI projects 🤖 This repository consolidates significant resources in a single location, including frameworks, training tools, inference utilities, RAG solutions, agents, and more. The content is organized into distinct categories to facilitate efficient navigation and resource identification for specific tasks. 📂 Repo: https://github.com/alvinreal/awesome-opensource-ai Tags: #github #useful ✔️

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📝 12 Essential Articles for Data Scientists 🏷 Article: Seq2Seq Learning with NN https://arxiv.org/pdf/1409.3215 An introduction to Seq2Seq models, which serve as the foundation for machine translation utilizing deep learning. 🏷 Article: GANs https://arxiv.org/pdf/1406.2661 An introduction to Generative Adversarial Networks (GANs) and the concept of generating synthetic data. This forms the basis for creating images and videos with artificial intelligence. 🏷 Article: Attention is All You Need https://arxiv.org/pdf/1706.03762 This paper was revolutionary in natural language processing. It introduced the Transformer architecture, which underlies GPT, BERT, and contemporary intelligent language models. 🏷 Article: Deep Residual Learning https://arxiv.org/pdf/1512.03385 This work introduced the ResNet model, enabling neural networks to achieve greater depth and accuracy without compromising the learning process. 🏷 Article: Batch Normalization https://arxiv.org/pdf/1502.03167 This paper introduced a technique that facilitates faster and more stable training of neural networks. 🏷 Article: Dropout https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf A straightforward method designed to prevent overfitting in neural networks. 🏷 Article: ImageNet Classification with DCNN https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf The first successful application of a deep neural network for image recognition. 🏷 Article: Support-Vector Machines https://link.springer.com/content/pdf/10.1007/BF00994018.pdf This seminal work introduced the Support Vector Machine (SVM) algorithm, a widely utilized method for data classification. 🏷 Article: A Few Useful Things to Know About ML https://homes.cs.washington.edu/~pedro/papers/cacm12.pdf A comprehensive collection of practical and empirical insights regarding machine learning. 🏷 Article: Gradient Boosting Machine https://www.cse.iitb.ac.in/~soumen/readings/papers/Friedman1999GreedyFuncApprox.pdf This paper introduced the "Gradient Boosting" method, which serves as the foundation for many modern machine learning models, including XGBoost and LightGBM. 🏷 Article: Latent Dirichlet Allocation https://jmlr.org/papers/volume3/blei03a/blei03a.pdf This work introduced a model for text analysis capable of identifying the topics discussed within an article. 🏷 Article: Random Forests https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf This paper introduced the "Random Forest" algorithm, a powerful machine learning method that aggregates multiple models to achieve enhanced accuracy.

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✔️ 10 Books to Understand How Large Language Models Function (2026) 1. Deep Learning https://deeplearningbook.org The definitive reference for neural networks, covering backpropagation, architectures, and foundational concepts. 2. Artificial Intelligence: A Modern Approach https://aima.cs.berkeley.edu A fundamental perspective on artificial intelligence as a comprehensive system. 3. Speech and Language Processing https://web.stanford.edu/~jurafsky/slp3/ An in-depth examination of natural language processing, transformers, and linguistics. 4. Machine Learning: A Probabilistic Perspective https://probml.github.io/pml-book/ An exploration of probabilities, statistics, and the theoretical foundations of machine learning. 5. Understanding Deep Learning https://udlbook.github.io/udlbook/ A contemporary explanation of deep learning principles with strong intuitive insights. 6. Designing Machine Learning Systems https://oreilly.com/library/view/designing-machine-learning/9781098107956/ Strategies for deploying models into production environments. 7. Generative Deep Learning https://github.com/3p5ilon/ML-books/blob/main/generative-deep-learning-teaching-machines-to-paint-write-compose-and-play.pdf Practical applications of generative models and transformer architectures. 8. Natural Language Processing with Transformers https://dokumen.pub/natural-language-processing-with-transformers-revised-edition-1098136799-9781098136796-9781098103248.html Methodologies for constructing natural language processing systems based on transformers. 9. Machine Learning Engineering https://mlebook.com Principles of machine learning engineering and operational deployment. 10. The Hundred-Page Machine Learning Book https://themlbook.com A highly concentrated foundational overview without extraneous detail. 📚🤖

🚀 Sber has released two open-source MoE models: GigaChat-3.1 Ultra and Lightning Both code and weights are available under the MIT license on HuggingFace. 👉 Key details: • Trained from scratch (not a finetune) on proprietary data and infrastructure • Mixture-of-Experts (MoE) architecture Models: 🧠 GigaChat-3.1 Ultra • 702B MoE model for high-performance environments • Outperforms DeepSeek-V3-0324 and Qwen3-235B on math and reasoning benchmarks • Supports FP8 training and MTP ⚡️ GigaChat-3.1 Lightning • 10B model (1.8B active parameters) • Outperforms Qwen3-4B and Gemma-3-4B on Sber benchmarks • Efficient local inference • Up to 256k context Engineering highlights: • Custom metric to detect and reduce generation loops • DPO training moved to native FP8 • Improvements in post-training pipeline • Identified and fixed a critical issue affecting evaluation quality 🌍 Trained on 14 languages (optimized for English and Russian) Use cases: • chatbots • AI assistants • copilots • internal ML systems Sber provides a solid open foundation for developers to build production-ready AI systems with lower infrastructure costs.

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