Books

Personally, I think that the Assembly system of conditional jumps makes a lot of sense. Other programming languages such as BASIC and C have "goto" statements that work like this. For example, `if(eax<ebx){goto less;}`. Modern programming languages tend to discourage the use of goto or not allow it at all. However, these languages still use jumps I have described in this section because it is required by the hardware. Both "if" and "while" statements are written by using the conditional jump statements most relevant to what you are trying to do. The only thing I have found difficult about jumps in assembly is remembering which acronym means which condition. However, since I created the chart in this chapter, now I can refer to it, and you can too! As long as I keep these six main types of conditions in my head and am working with unsigned numbers, I can write almost any assembly program from scratch.

Artificial Intelligence is no longer a distant possibility—it is becoming a collaborative partner in education, research, software development, and professional decision-making.But how do frontier AI systems actually work?What makes safety-centered AI different from earlier generations of language models?How can students, researchers, educators, and professionals prepare for a future shaped by increasingly capable AI systems?In Claude 4 (Anthropic): Safety-First Frontier AI in 2026, author Anshuman Mishra presents a comprehensive and academically grounded exploration of one of the most influential frontier AI systems of the modern era.From Constitutional AI and advanced reasoning architectures to multimodal intelligence, agentic workflows, educational transformation, governance frameworks, workforce implications, and future AI research directions, this book provides readers with a balanced understanding of both opportunities and challenges.Rather than focusing on hype or speculation, the book emphasizes evidence-based analysis, responsible innovation, ethical deployment, and human-centered AI development.Whether you are a student seeking AI literacy, a researcher exploring frontier models, a professional adapting to technological change, or a policymaker shaping future governance frameworks, this book offers the knowledge needed to engage thoughtfully with the next generation of intelligent systems.The future of AI will not be determined by technology alone.It will be shaped by how responsibly humanity chooses to use it.

This Developer's Guide is the "Project #1" from the also included "Building Role-Playing Browser Games" workbook. It leads you through the development of the "St. George Battles the Dragons" Role-Playing Game in both Construct v3 & v2 encoding. It's perfect for novices, experienced web developers, and anyone wanting their own bespoke game, artwork, and features.

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.

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

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.