Books

Aprende a combinar SQL, Python, estadística y Machine Learning para priorizar posibles fraudes en retail. Un enfoque práctico, técnico y aplicado, con ejemplos, visualizaciones y códigos de apoyo.

不需要机器人实验室，也能真正上手具身智能。

A 1,032-page study system for the AIGP exam: every question explained option by option, then linked to the Concept Card that makes it answerable.

Most self-hosted LLM deployments run at 10–30% utilization — full retail price for GPUs that mostly sit idle. Tokens per Dollar is a deliberately short field guide for platform engineers: tune vLLM (2–4x throughput on the same hardware), quantize to halve your GPU count, cache the prompt tokens you keep re-paying for, and autoscale on real token traffic instead of CPU. Working YAML throughout, and a case study that cuts a 70B deployment from $29,200 to $8,600 a month with better latency. Read it in an evening; it pays for itself the first day you apply it.

AI can produce more work in less time. Making that work reliable takes discipline. Disciplined AI Development explores how to build workflows that combine AI capabilities with human judgment, clear boundaries, and rigorous verification.

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.

Build real machine learning applications with TensorFlow.js, from your first model to production-ready systems. Learn how to train and deploy models in the browser and Node.js, tackle real-world challenges and explore modern techniques with practical JavaScript and TypeScript examples you can run and adapt.

Forecasting is changing fast. This practical guide takes you from ARIMA and exponential smoothing to Transformers, PatchTST and foundation models like Chronos and TimesFM. With clear explanations, hands-on Python examples and an honest look at what works and what fails, you’ll learn how to build forecasting systems that hold up in the real world.

My pitch was that rules written in CLAUDE.md get skipped, so you need enforcement. Then I measured it. In 22 trials with the ban written in, the banned command was never attempted. In 17 without it, 17 out of 17. This is the whole record: the instrument, every trial, the limits, and the harness so you can check me.

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.

"I am not qualified to endorse theology. But I can witness: the structure is real. It was discovered, not invented."

The Local AI Stack: Building a Sovereign Machine Learning Workstation with Hyper-V, WSL2, and GPU Virtualization Renting GPU time from AWS costs more than the GPU. Every prompt you send to a cloud API is a prompt someone else logs. If your ML work runs on hardware you don't own, on a network you don't control, then you don't own your ML work.

Mastering NVIDIA CUDA: Expert GPU Programming About the BookTo master NVIDIA CUDA at an expert level, you must discard the outdated notion of the GPU as a mere "parallel co-processor" and instead view it as a highly integrated, self-orchestrating compute fabric.

The Quantization Black Book The definitive engineering reference for 4-bit quantization of large language models — from 70B all the way to 400B parameters. Written for the people who actually have to fold these models onto real hardware and keep them fast, accurate, and deployable. This is not an overview. It is a deep technical field manual covering the full quantization stack: the math, the algorithms, the hardware setup, the calibration process, the runtime kernels, and the memory tricks that make the difference between a model that runs and a model that doesn't.

📘 Advancing Technical AI via Curriculum Reasoning — 119‑Page Engineering BlueprintA research‑grade handbook for building high‑precision reasoning models through structured curriculum design.This 119‑page ebook is a complete, end‑to‑end framework for Machine Learning Engineers, AI Researchers, and systems architects who want to push LLMs beyond pattern matching and into true inferential reasoning.