Ronald T. Kneusel Libros

Math for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits.With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning. You’ll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You’ll also use Python to work through the mathematics that underlies those algorithms and even build a fully-functional neural network.In addition you’ll find coverage of gradient descent including variations commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad/Adadelta.

Focusing on pseudorandom number generation, the book delves into various algorithms and evaluation techniques essential for understanding randomness in computing. It provides practical advice, accompanied by code examples, to help readers implement these concepts effectively. The content is designed to enhance both theoretical knowledge and practical skills in generating and evaluating pseudorandom numbers.

AI isn’t magic. How AI Works demystifies the explosion of artificial intelligence by explaining—without a single mathematical equation—what happened, when it happened, why it happened, how it happened, and what AI is actually doing "under the hood." Artificial intelligence is everywhere—from self-driving cars, to image generation from text, to the unexpected power of language systems like ChatGPT—yet few people seem to know how it all really works. How AI Works unravels the mysteries of artificial intelligence, without the complex math and unnecessary jargon. You’ll learn: The relationship between artificial intelligence, machine learning, and deep learning The history behind AI and why the artificial intelligence revolution is happening now How decades of work in symbolic AI failed and opened the door for the emergence of neural networks What neural networks are, how they are trained, and why all the wonder of modern AI boils down to a simple, repeated unit that knows how to multiply input numbers to produce an output number. The implications of large language models, like ChatGPT and Bard, on our society—nothing will be the same again AI isn’t magic. If you’ve ever wondered how it works, what it can do, or why there’s so much hype, How AI Works will teach you everything you want to know.

Strengthen your overall coding skills by exploring the wonderful, wild, and often weird world of esoteric languages (esolangs). Strange Code starts with a dive into the underlying history of programming, covering the early computer-science concepts, like Turing machines and Turing completeness, that led to the languages we use today. It then explores the realm of “atypical” programming languages, introducing you to the out-of-the-box thinking that comes from these unusual approaches to coding. Later chapters address the even more unusual esolangs, nearly all of which are like nothing you’ve ever seen. Finally, author Ron Kneusel helps you develop and use two entirely new programming languages. You may not apply these languages in your day job, but this one-of-a-kind book will motivate you to think differently about what it means to express thought through code, while discovering the far-flung boundaries of programming. You’ll learn: How to program with pictures using Piet How to write two-dimensional programs in Befunge How to implement machine-learning algorithms using the text pattern matching language SNOBOL How to decipher Brainfuck code like [->-[>+”]>[[-+]+>+”]““]/liliHow to design and create two original programming languages Learning to think in these languages will make you a better, more confident programmer.

This is a book about numbers and how those numbers are represented in and operated on by computers. It is crucial that developers understand this area because the numerical operations allowed by computers, and the limitations of those operations, especially in the area of floating point math, affect virtually everything people try to do with computers. This book aims to fill this gap by exploring, in sufficient but not overwhelming detail, just what it is that computers do with numbers. Divided into two parts, the first deals with standard representations of integers and floating point numbers, while the second examines several other number representations. Details are explained thoroughly, with clarity and specificity. Each chapter ends with a summary, recommendations, carefully selected references, and exercises to review the key points. Topics covered include interval arithmetic, fixed-point numbers, big integers and rational arithmetic. This new edition has three new chapters: Pitfalls of Floating-Point Numbers (and How to Avoid Them), Arbitrary Precision Floating Point, and Other Number Systems. This book is for anyone who develops software including software engineers, scientists, computer science students, engineering students and anyone who programs for fun.