Books

WHO SHOULD READ THIS BOOK?This book is ideal for:·        BCA, MCA, B.Tech, M.Tech students·        UGC NET aspirants·        AI/ML researchers·        Data scientists·        AI developers·        University professors·        PhD scholars·        Industry professionals working with black-box models·        Anyone who wants mathematical clarity on XAIIts writing style balances mathematical rigor with readability, making it useful for self-study and classroom use. TEACHING & LEARNING BENEFITS·        50+ diagrams, proofs, and mathematical derivations.·        Step-by-step logical flow for each model.·        Case studies from healthcare, finance, law, and engineering.·        Practical coding references (without over-reliance on tools).·        Integration of statistics, calculus, causality, and deep learning.·        Real-world examples for intuitive understanding.·        Problems at the end of each chapter (optional addition).Instructors can adopt this book for academic courses in:·        Explainable AI·        Machine Learning·        Statistical Inference·        Causality·        Artificial Intelligence Foundations·        Deep Learning Interpretability UNIQUE CONTRIBUTIONS OF THIS BOOKUnlike other XAI or ML books, this work by Anshuman Mishra offers:·        Mathematical derivations for SHAP, IG, LIME, and other explainability tools.·        Original proofs for fairness properties in attribution methods.·        Detailed causal diagrams and do-calculus explanations.·        A structured approach to XAI evaluation metrics.·        Coverage of transformer explainability—rare in academic books.·        Clarity in blending classical mathematical theory with modern AI systems.This makes the book a reference-level resource for the next decade of AI learning.

Geopolitics is too late. Discover how the Naciro Engine uses AI to measure the pulse of nations in real-time, providing a 15-minute recalibration of global truth and power. Check the live data on https://nationfiles.com

From quantum and molecules to cells, individuals, organizations, nations, and civilizations—why do clearly defined hierarchical structures emerge? Why does evolution often manifest as a repeating rhythm of "oscillation—stability—aggregation—re-stability"? Why do many systems fail not because of insufficient power or lack of information, but because the "pace of doing things" is wrong?

Master deep learning with PyTorch & Lightning. Core concepts, practical Q&A, and production-ready code with a full companion GitHub repository.

This book is a quick foray into the world of deep learning-based computer vision and abnormal equipment sound detection. The readers are introduced to the ease with which powerful equipment and product quality monitoring solutions can be built using sound and visual data.

From a single neuron to transformer-based LLMs in production — the deep-learning, NLP, and shipping capstone of the Data Science, End to End series, derived from first principles in Python.

About the Book Running an LLM is one problem. Understanding its memory footprint, execution phases, and serving behavior is another.LLM Inference Engine Architecture explores the engineering behind language-model inference—from prefill and decode to KV-cache management, attention kernels, quantization, and request scheduling.Written for software engineers and ML engineers with a working knowledge of Python and transformer fundamentals, the book connects mathematical explanations with implementation examples and serving-system design considerations.What You Will ExploreInference fundamentals: Trace the request lifecycle and understand the different computational characteristics of prefill and decode.Memory budgeting: Work through model-weight and KV-cache estimates, including the effects of context length, KV-head count, precision, and concurrent sequences.Attention and memory management: Explore PagedAttention, non-contiguous KV allocation, and the relationship between attention computation and memory access.Quantization: Examine weight and KV-cache precision choices, including FP8 and low-bit weight-quantization approaches.Serving architecture: Study continuous batching, request scheduling, multi-GPU execution, and latency and throughput metrics.Kernel development: Explore CUDA and Triton concepts, kernel fusion, and a quantized matrix-multiplication case study.The book includes numerical examples, reference code, and an educational asynchronous serving simulator. These examples are intended to explain mechanisms and trade-offs—not to replace hardware-specific profiling or production validation.Read the sample to review the mathematical level, coding style, and scope before purchasing.

هذا الكتاب هو دليل مختصر وموضّح بالرسوم لأي شخص يرغب في فهم الآلية الداخلية للنماذج اللغوية الضخمة، سواء في سياق المقابلات أو المشاريع أو بدافع الفضول الشخصي.

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.

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.

Build a Stronger Technical Brand. Write Better Code. Publish with Confidence.The Official Ahmed Adawy Tech Capsules Guide is a practical resource for creating clear, consistent, and professional technical content. Learn how to structure technical explanations, present code examples more effectively, maintain technical accuracy, and build a recognizable identity for your educational and publishing projects.

Every news feed is full of AI buzzwords: Transformers, tokens, embeddings, context windows, hallucinations, objective functions. Yet most explanations are either dense academic textbooks or empty marketing fluff. ☕ Coffee Break AI is your practical guide to AI. Written in plain English with warm real-world analogies. It breaks down the core mechanisms of AI into 40 bite-sized chapters.

We'll stick to five libraries, not because more would be a problem, but because keeping it simple shows how well we can organise things. When you download MNIST with just the standard library, you finally see what a dataset loader was hiding. If you write attention as four lines of NumPy before you ever call a PyTorch module, it's no longer a magic process but just plain arithmetic.

Deep learning is transforming the world, and this book provides a clear, practical path to mastering it. Through concise explanations and hands-on Python examples, you will learn to build, train, optimize, and deploy neural networks with confidence for real-world applications.