Bøger af Ronald T. Kneusel

"An accessible, straightforward guide that demystifies Artificial Intelligence for a general audience without the use of complex math or technical jargon. Covers the fundamentals, from classical models and neural networks to the large language models leading today's AI revolution"--

Learn all of the core mathematical topics that professional software engineers need to know—in a single book!This book summarizes all the core mathematical topics a typical professional software engineer needs to know. In condensing the various concepts covered in an undergraduate computer science program into a single volume, it provides an excellent starting point for independent study, or a refresher for those who haven’t been in a classroom for years. Early chapters cover preliminary subjects like number representation systems, set theory, and Boolean algebra, followed by a dive into the field of discrete mathematics, including functions, induction proofs, number theory, combinatorics, graphs, and trees. Later sections examine essential topics in probability, statistics, linear algebra, and calculus.Rather than confine itself to abstract theory, the book focuses on practical applications and numerical methods at the level typically encountered by working software developers. In addition, hands-on code examples in Python and C make the topics concrete.

"The Art of Randomness teaches readers to harness the power of randomness (and Python code) to solve real-world problems in programming, science, and art through hands-on experiments-from simulating evolution to encrypting messages to making machine-learning algorithms. Each chapter describes how randomness plays into the given topic area, then proceeds to demonstrate its problem-solving role with hands-on experiments to work through using Python code"--

Strengthen your coding skills by exploring the weird world of esoteric programming languages.Explore the wonderful, wild, and often weird world of esoteric programming languages. The book begins with the history and theory of programming languages, addressing concepts like Turing machines and Turing completeness. You're then treated to a tour of three "atypical" programming languages, real languages that are unusual and require out of the box thinking. Following that are five chapters on existing esoteric languages (esolangs), some of which are easy to use, others quite difficult, and others novel because of their approach (programming with pictures, for example). Finally, the remaining chapters detail the development and use of two entirely new programming languages. The main point of the book is to encourage readers to think differently about what it means to express thought using a programming language, and to explore the limits and boundaries of what a programming language might be. Though readers aren't likely to use any of these languages in their day jobs, learning to think in these languages will make them better, more confident programmers.

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.

This is a book about numbers and how those numbers are represented in and operated on by computers.

This book covers pseudorandom number generation algorithms, evaluation techniques, and offers practical advice and code examples.

This book covers pseudorandom number generation algorithms, evaluation techniques, and offers practical advice and code examples.