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.
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.
Harness the power of randomness (and Python code) to solve real-world problems in fun, hands-on experiments—from simulating evolution to encrypting messages to making machine-learning algorithms! The Art of Randomness is a hands-on guide to mastering the many ways you can use randomized algorithms to solve real programming and scientific problems. You’ll learn how to use randomness to run simulations, hide information, design experiments, and even create art and music. All you need is some Python, basic high school math, and a roll of the dice. Author Ronald T. Kneusel focuses on helping you build your intuition so that you’ll know when and how to use random processes to get things done. You’ll develop a randomness engine (a Python class that supplies random values from your chosen source), then explore how to leverage randomness to: Simulate Darwinian evolution and optimize with swarm-based search algorithms Design scientific experiments to produce more meaningful results by making them truly random Implement machine learning algorithms like neural networks and random forests Use Markov Chain Monte Carlo methods to sample from complex distributions Hide information in audio files and images, generate art, and create music Reconstruct original signals and images from only randomly sampled data Scientific anecdotes and code examples throughout illustrate how randomness plays into areas like optimization, machine learning, and audio signals. End-of-chapter exercises encourage further exploration. Whether you’re a programmer, scientist, engineer, mathematician, or artist, you’ll find The Art of Randomness to be your ticket to discovering the hidden power of applied randomness and the ways it can transform your approach to solving problems, from the technical to the artistic.
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.
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.
Harness the power of randomness (and Python code) to solve real-world problems in fun, hands-on experiments—from simulating evolution to encrypting messages to making machine-learning algorithms! The Art of Randomness is a hands-on guide to mastering the many ways you can use randomized algorithms to solve real programming and scientific problems. You’ll learn how to use randomness to run simulations, hide information, design experiments, and even create art and music. All you need is some Python, basic high school math, and a roll of the dice. Author Ronald T. Kneusel focuses on helping you build your intuition so that you’ll know when and how to use random processes to get things done. You’ll develop a randomness engine (a Python class that supplies random values from your chosen source), then explore how to leverage randomness to: Simulate Darwinian evolution and optimize with swarm-based search algorithms Design scientific experiments to produce more meaningful results by making them truly random Implement machine learning algorithms like neural networks and random forests Use Markov Chain Monte Carlo methods to sample from complex distributions Hide information in audio files and images, generate art, and create music Reconstruct original signals and images from only randomly sampled data Scientific anecdotes and code examples throughout illustrate how randomness plays into areas like optimization, machine learning, and audio signals. End-of-chapter exercises encourage further exploration. Whether you’re a programmer, scientist, engineer, mathematician, or artist, you’ll find The Art of Randomness to be your ticket to discovering the hidden power of applied randomness and the ways it can transform your approach to solving problems, from the technical to the artistic.
Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects. If you’ve been curious about machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math—the book will cover the rest. After an introduction to Python, you’ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models’ performance. You’ll also learn: How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines How neural networks work and how they’re trained How to use convolutional neural networks How to develop a successful deep learning model from scratch You’ll conduct experiments along the way, building to a final case study that incorporates everything you’ve learned. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.
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.
This book covers pseudorandom number generation algorithms, evaluation techniques, and offers practical advice and code examples. Random Numbers and Computers is an essential introduction or refresher on pseudorandom numbers in computer science. The first comprehensive book on the topic, readers are provided with a practical introduction to the techniques of pseudorandom number generation, including how the algorithms work and how to test the output to decide if it is suitable for a particular purpose. Practical applications are demonstrated with hands-on presentation and descriptions that readers can apply directly to their own work. Examples are in C and Python and given with an emphasis on understanding the algorithms to the point of practical application. The examples are meant to be implemented, experimented with and improved/adapted by the reader.
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.
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.
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