Computer Programming/Artificial Intelligence/Python

Your LLM pipeline passed every check, and a fallback model still served 50 of its 71 successful calls. This case study follows one real multi-stage pipeline through its failures and shows how to make each stage traceable, attributable and honestly measured.

Your Sharpe ratio is 2.8. Your model is wrong.Not because the code is broken, but because somewhere between the raw data and the equity curve, the future leaked into the past. A centered rolling window. A normalization fit on the full sample. A fundamentals join on the wrong date. Each one runs clean, and each one produces a number that disappears the week real money replaces history.Machine Learning for Market Prediction is a quant reviewer’s playbook for building a pipeline you can actually trust: point in time data, leakage-free features, triple barrier labels, embargoed walk-forward validation, realistic costs, and SHAP audits that trace a suspicious feature to its root cause.It will not make your model more profitable. It will tell you the truth about it.Distrust your backtest. Then prove it right.

Your AI application works on your machine. What happens when it reaches production? Learn to deploy, monitor, secure, and scale AI systems using Docker, CI/CD, Kubernetes, cloud infrastructure, and practical production engineering. Move beyond prototypes and build AI applications you can operate with confidence.

Build a real document management system from scratch to production: OCR, keyword and semantic search, an AI assistant that cites its sources, sharing and approvals, Docker, Nginx and private HTTPS.

Build agents that write code, fix tests, and commit changes while you sleep — safely, cheaply, and entirely on your own hardware (622 manuscript pages).

The ultimate bridge from high school AP Physics to modern AI. Start with the familiar physics you know, translate it into Python, and use it as a launchpad to build advanced Machine Learning models and neural networks. Includes 100% solved exercises and click-to-run Google Colab labs.

AI can decide. That does not mean it is authorized to act. Governance Before Decision examines the missing control layer between intelligent systems and real-world consequences — where authority, scope, verification, and execution must meet.

Build 10 Exciting Games and Explore Professional Game Development with PythonLearn game development through 10 hands-on projects—from Pong, Breakout, and Platformers to RPGs and multiplayer games—while exploring game mechanics, AI, networking, testing, optimization, Git, and real-world development practices.

Go beyond calling transformer libraries and learn what actually happens under the hood. Starting from first principles, this book builds transformers from scratch with NumPy and PyTorch, then takes you through training, optimization and production inference. Along the way, you’ll work through the math, code and practical techniques behind modern transformer systems.

Your AI assistant forgets you every time the session ends. Building Persistent AI shows you how to fix that — by moving memory, personality, and identity out of the model and into a file you own and control.

Learn Python from the ground up with clear explanations, practical examples, and a beginner-friendly approach. Build a strong foundation in programming and gain the confidence to move toward real-world Python projects.

Abandona el ensayo y error en la IA generativa. Un marco práctico y metodológico basado en el falsacionismo de Karl Popper para auditar, estresar y dominar la interacción con modelos de lenguaje.

A hands-on guide for engineers and architects to build, optimize, and deploy scalable production-grade AI systems and LLM infrastructure.

The second half of a from-scratch AI/ML course that refuses to skip the hard parts: LSTMs, real attention, a working transformer, LoRA fine-tuning derived and measured, and a genuine multi-tool, multi-agent system — every mechanism built by hand, then checked against PyTorch and real numbers.