Machine Learning with Python

✔️ 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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A new open-source Python library titled "Fli" has been released, offering direct access to Google Flights. This library circumvents the web interface by interfacing directly with a reverse-engineered API to deliver rapid and structured results. The project is 100% open-source. 100% open-source.

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🚀 Fine-Tuning Large Language Models for Domain-Specific Tasks Fine-tuning Large Language Models is the process by which generic LLMs are transformed into domain-specific experts. This procedure updates model weights using task-specific labeled data, rather than relying solely on prompting or retrieval mechanisms. This approach is particularly effective when language patterns remain stable and consistent outputs are required. 👉 Core Concept A pre-trained LLM acquires general language capabilities. Fine-tuning instructs the model on how language functions within specific domains, such as healthcare, finance, legal services, or internal enterprise workflows. 👉 Practical Implementation A customer support model is trained on thousands of instruction-response pairs. For example: Input: Refund request for a delayed shipment Output: A policy-compliant response including an apology, procedural steps, and a resolution. Following fine-tuning, the model generates consistent, policy-aligned answers with lower latency compared to Retrieval-Augmented Generation (RAG). 👉 Significance of Parameter-Efficient Fine-Tuning Techniques such as LoRA and QLoRA train only small adapter layers while keeping the base model frozen. This methodology reduces GPU memory consumption, accelerates training, and enables the fine-tuning of large models on hardware with limited resources. 👉 Appropriate Use Cases for Fine-Tuning - Recurring domain-specific language - Structured outputs, including classifications, summaries, or templates - Stable knowledge bases that do not undergo daily changes - Latency-sensitive systems where retrieval introduces overhead Typical Production Stack - Models: LLaMA or Mistral - Frameworks: PyTorch with Hugging Face and PEFT - Optimization: DeepSpeed or Accelerate - Deployment: FastAPI, Docker, and cloud GPUs 💡 Fine-tuning enhances accuracy, consistency, and cost efficiency when applied to suitable problems.

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Build a Large Language Model from Scratch! 🚀 This repository provides code examples for developing, pretraining, and fine-tuning a Large Language Model (LLM) from the ground up. It serves as the official codebase for the book "Build a Large Language Model (From Scratch)." 📘 Notebook examples are included for each chapter: Chapter 1: Understanding Large Language Models 🧠 Chapter 2: Working with Text Data 📝 Chapter 3: Coding Attention Mechanisms ⚙️ Chapter 4: Implementing a GPT Model from Scratch 🏗 Chapter 5: Pretraining on Unlabeled Data 📊 Chapter 6: Fine-tuning for Text Classification 🏷 Chapter 7: Fine-tuning to Follow Instructions 🗣 Repository: https://github.com/rasbt/LLMs-from-scratch 🔗

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