The publishing house BHV recommends focusing on fundamental AI concepts rather than specific tools or frameworks, which may become obsolete. Essential areas for study include machine learning basics, neural network architecture, data processing, and system integration.
Lawrence Moroney’s book 'AI and Machine Learning for Coders' (O'Reilly, 2025) emphasizes a practical approach to PyTorch, computer vision, and transformers. Andrey Burkov’s 'Language Models' provides a deep dive into the mechanics of LLMs, including attention mechanisms and fine-tuning.
Jay Alammar and Maarten Grootendorst’s 'Hands-On Large Language Models' (O'Reilly, 2024) covers semantic search and RAG, holding a high ranking in NLP categories. Suhas Pai’s 'Designing Large Language Model Applications' (O'Reilly, 2025) focuses on the engineering side, including latency, cost, and infrastructure.
Nuno Campos and Mayo Oshin’s book on LangChain serves as a guide for understanding agent architectures and orchestration. Additionally, 'Hands-On Generative AI with Transformers and Diffusion Models' by Pedro Cuenca et al. explores multimodal generation, including audio and image synthesis.
