HHSHOP68

Probabilistic Machine Learning Advanced Topics by Kevin Murphy that covers cutting-edge machine learning topics like deep generative models, Bayesian inference, and causality. It unifies deep learning with statistical context, providing a rigorous guide for researchers and students, complete with extensive Python code examples.

x64 Assembly Language Step-by-Step (4th Edition) by Jeff Duntemann is a comprehensive guide to mastering 64-bit Intel assembly on Linux. It offers a lighthearted, accessible path to understanding x64 architecture, low-level instruction sets, and practical software development using open-source tools.

Generative AI Design Patterns offers 32 essential solutions for building reliable AI agents. Authors Valliappa Lakshmanan and Hannes Hapke address challenges like hallucinations and knowledge cutoffs with practical, coded patterns for improving content style, model capabilities, reliability, and agentic workflows.

Effective Python with 125 actionable best practices in this updated 3rd edition. Covering Python 3.13, Brett Slatkin provides expert guidance on writing 'Pythonic' code, improving performance, and building robust programs. Essential for both new and experienced developers seeking to leverage the language's full potential.

Deep Learning Crash Course is a project-based guide to building AI models from scratch using PyTorch. Designed for programmers, it covers CNNs, Transformers, GNNs, and reinforcement learning through hands-on examples. Move from using AI tools to creating them with this comprehensive 680-page masterclass.

Grokking AI Algorithms 2nd Edition is a visually-driven guide to AI core algorithms. It uses relatable illustrations and hands-on examples to explain deep learning, reinforcement learning, and generative AI models (LLMs), requiring only basic programming and high school math skills to master complex concepts.