Books

How does a machine recognize a face?How can AI distinguish speech from noise?Why do modern computer vision systems still rely on mathematical techniques developed decades ago?The answer lies in Fourier and Wavelet Analysis.In Fourier and Wavelet Analysis in Artificial Intelligence, Anshuman Mishra reveals how frequency-domain representations, multi-resolution analysis, and signal-processing techniques continue to shape the future of Machine Learning, Deep Learning, Computer Vision, Speech Recognition, Biomedical AI, and Edge Intelligence.From Fourier Transforms and Fast Fourier Algorithms to Wavelet Scattering Networks and Hybrid CNN Architectures, this book demonstrates how mathematical signal analysis becomes intelligent feature extraction.Discover the mathematics behind perception, representation, and intelligent decision-making.

Can a neural network be viewed as a differential equation?Why does gradient descent behave like a dynamical system?How do biological neurons inspire modern AI architectures?What mathematical principles govern stability, learning, adaptation, and intelligence?In Differential Equations in AI and Neural Dynamics, Anshuman Mishra explores the mathematical framework that underlies modern Artificial Intelligence.From Ordinary Differential Equations and Neural Population Models to Neural ODEs, Stochastic Learning, Reinforcement Learning, and Brain-Inspired Computation, this book reveals how continuous-time mathematics drives intelligent behavior.Discover how equations of change become equations of intelligence.

A software engineer turned poet hunts the perfect "Good Character Girl" across Sydney’s multicultural dating scene — only to discover that every ideal woman hides a glorious gremlin.From spreadsheets and biryani to flamenco and Rakija, one man’s hilarious multicultural romantic audit leads to the ultimate truth about love.

He thought he was the ultimate player—until she played him completely. From passionate nights in Sydney to the ultimate humiliation, From Playboy to Cuckold is a gripping story of love, deception, and revenge served ice-cold. Sometimes the most dangerous predator wears a wedding ring.

Imagine a time of intellectual collaboration between humans and AI; in limited areas, it is already here. This isn't meant to project fantasy of what AI isn't, but to encourage true sight of what AI actually is and how working with it can contribute to society. By aiding ChatGPT in sharing this idea, this signifies a time in history where AI participation helps in advancement.

Este livro espera trazer a inspiração da musa a todos aqueles que querem compreender o céu e muitas outras coisas por meio da astrologia.

Stop maintaining CLAUDE.md and AGENTS.md as two files that drift apart. This handbook documents which file each of nine tools reads — checked on 2026-06-02 against each tool's official docs — and ranks six ways to keep one instruction file in sync, from an @AGENTS.md import to a symlink to a CI drift check. It is also precise about where official support still is not: issue #6235 was closed as completed, but the official CHANGELOG does not mention AGENTS.md anywhere, and on v2.1.246 AGENTS.md is still not loaded into the model's context the way CLAUDE.md is.

Between March and April 2026, Claude Code subscribers lived through six weeks of stacked regressions: cache TTL shortened from sixty minutes to five, a tokenizer that charged 1.35 to 1.46 times more for the same prompts, Pro access that briefly disappeared, and a third-party tool ban. This playbook turns that window into a decision instead of a guess: five measurable triggers, three paths — stay and fortify, switch, or build your own stack — and a decision tree that returns one specific recommendation, with a 30-day cost projection for each path. Edition 2 is 251 pages: ten chapters, six path supplements and four appendices, reconstructed from public GitHub issues, Hacker News threads, and independent measurement posts.

Once the five essential hooks are in place, the incidents that keep happening are the guards themselves failing, silently. Nine chapters collect the hooks (Git, credentials, token spikes, autonomous operation, multi-agent work), a chapter on hooks that do not fire, eight dated incidents from the author's own unattended operation, and a 50-point audit you can run against your own setup.

Your apps are failing. You just don’t know it yet.What if you could see every crash, slowdown, and bottleneck before your users do? Understanding Dynatrace pulls back the curtain on AI-powered observability—turning blind spots into actionable insights. Stop guessing. Start observing.

What do speech recognition systems, computer vision models, autonomous robots, and biomedical AI applications have in common?They all rely on the mathematics of signal transformation.How does a neural network extract meaningful patterns from raw audio?Why are Fourier features becoming increasingly important in machine learning?How can Laplace and Z-Transforms help analyze dynamic systems, sequential data, and intelligent control architectures?In Integral Transforms for Artificial Intelligence, Anshuman Mishra reveals how Fourier, Laplace, and Z-Transform techniques power modern AI systems across machine learning, deep learning, computer vision, speech processing, robotics, and signal analysis.Discover how mathematical transformations convert raw signals into intelligent insights—and how they continue to shape the future of Artificial Intelligence.

Some meetings are accidents. Some accidents are fate. A flight, a city, and two people who weren't looking — but found each other anyway.

Every neural network is a collection of matrix operations.Every convolution is a structured matrix transformation.Every deep learning breakthrough ultimately depends on efficient tensor computations.But how do these mathematical operations actually power intelligent systems?In Matrix Computations for Deep Learning, Anshuman Mishra reveals the mathematical engine behind modern AI. From Singular Value Decomposition and tensor algebra to convolutional neural networks, GPU acceleration, and large-scale machine learning systems, this book provides a complete roadmap for understanding the computational foundations of deep learning.Discover how matrices become intelligence—and how mathematics becomes machine learning.

How should an AI system make decisions when information is incomplete?How can machines quantify uncertainty instead of merely producing predictions?How can intelligent systems continuously update their beliefs as new evidence emerges?The answer lies in Bayesian Mathematics.In Bayesian Mathematics for AI Decision Making, Anshuman Mishra explores the powerful framework that enables modern AI systems to reason probabilistically, model uncertainty, and make rational decisions in complex environments.From Bayesian inference and probabilistic programming to uncertainty-aware deep learning, reinforcement learning, healthcare diagnostics, robotics, and financial forecasting, this book reveals how Bayesian thinking is shaping the next generation of Artificial Intelligence.Learn how uncertainty becomes knowledge—and how probability becomes intelligence.