Artificial Intelligence

AI can write code in seconds, but can you trust what it creates? Securing AI-Generated Code shows you how to uncover hidden vulnerabilities, stop risky dependencies, secure AI coding agents and build safer development pipelines. A practical guide for teams that want the speed of AI without putting security on the line.

Stop building brittle LLM wrappers and start engineering production-grade autonomous agent networks.This hands-on guide takes you from fundamental Transformer mechanics to orchestrating multi-agent architectures under Kubernetes with Python. Step beyond basic prototypes to master graph-based orchestration, grammar-constrained outputs, LoRA fine-tuning, RAG, and MCP. Learn to navigate critical production trade-offs—model routing, quantization, cost control, prompt injection security, and human-in-the-loop monitoring—to deploy resilient, enterprise-ready AI systems.

Explore the foundations and future of modern computing—from artificial intelligence and distributed systems to cybersecurity, quantum technologies, and responsible computing. Modern Computer Science connects essential concepts with emerging technologies, real-world applications, and the challenges shaping the future of the discipline.

Master LTX 2.5 from the first prompt to the final shot. Learn to create cinematic video with synchronized audio, precise camera movement and consistent characters while avoiding the mistakes that waste time and generations. Built on official documentation and verified behavior, this is a practical guide to getting production-ready results from LTX 2.5.

Use LoRA because it forgets less. Keep the learning rate low. Mix in some general data. Stopearly. Four pieces of advice everyone repeats and almost nobody quantifies. This book measures each one — and two turn out to be doing something other than what they arecredited with. LoRA's retention is governed by a scaling constant, not by its parameterefficiency. Early stopping bounds the damage without separating it from the gain. Everyexperiment reports two numbers: the new task, and the capability you were not trying tochange.

The model is only half the system. Learn to design the scaffolding, tools, sandboxes, verification loops, and memory that turn raw LLMs into reliable, production-grade AI agents that actually finish the job.

A hands-on guide to Anthropic's command-line AI coding assistant. Learn to install, converse with, and steer Claude Code through file edits, testing, debugging, refactors, and Git workflows, then build a real project from scratch and connect it to external tools with MCP.

The AI Advantage shows you how to use artificial intelligence to work smarter, create more, save time, boost productivity, and discover new opportunities—even if you’re completely new to AI. With practical tools, strategies, examples, and prompts, you’ll learn how to turn AI into a powerful advantage for your personal and professional growth.

AI has entered the programming picture, let's learn how to be humans working with AI to build better software, together.

Vector databases give you a similarity metric, an index with four tuning parameters, acompression option and a way to filter — and almost no guidance on what any of it costs. This book builds each one from scratch and measures it. The proximity graph, the invertedfile, the product quantiser: implemented in NumPy, included with the text, and checkedagainst exhaustive search — so every recall figure here is exact rather than estimated.

Both parts of the Transition Playbook in one 139-page volume: the mental models, the orchestration patterns, and two 30-day plans for moving from writing code to directing agents.

Four MCP servers installed end to end without a terminal, plus a troubleshooting decision tree and five workflows to run the day you finish.

Measure where LLM context costs accumulate, then cut them with deduplication, summarization, truncation and cache exploitation, with a cost model that shows the ROI of each.

A machine can hand you a grouping that is confident, stable, internally consistent, and seven times worse than the one it found a moment earlier. Nothing in the output says so.This book works k-means through completely on twelve bakery customers, small enough that every number is printed and checkable. You will build the method from nothing, see exactly where it succeeds, and see exactly where it fails silently.Fifty-eight figures, every one captured from a running implementation.

Every deep learning tutorial shows you the code. Almost none show you the idea. This is the book to read first - before the frameworks, before the maths notation, before the course you've already paid for. One example, followed all the way through, from a random guess to a network that works. By the end you'll understand what backpropagation actually does, why gradients matter, and what's really happening while your model trains. No calculus required.