Bücher von 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"--

"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.

To truly understand the power of deel learning, you need to grasp the mathematical concepts that make it tick. "Math for deep learning" will give you a working knowledge of probability, statistics, linear algebra, and differential calculus-- the essential math subfields required to practice deep learning successfully. Each subfield is explained with Python code and hands-on, real-world examples that bridge the gap between pure mathematics and its applications in deep learning. The book begins with fundamentals such as Bayes' theorem before progressing to more advanced concepts like training neural networks using vectors, matrices, and derivatives of functions. You'll then put all this math to use as you explore and implement backpropagation and gradient descent-- the foundational algorithms that have enabled the AI revolution.

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.

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