Ronald T. Kneusel – författare

Explore the wonderful, wild, and often weird world of esoteric programming languages. Beginning with the history and theory of programming languages, addressing concepts like Turing machines and Turing completeness. Then a tour of three 'atypical' programming languages, real languages that are unusual and require out of the box thinking. Then, five chapters on existing esoteric languages (esolangs). Finally, the remaining chapters detail the development and use of two entirely new programming languages.

When properly applied, randomness can be a powerful tool in programming, science, and art. This highly practical but geekily fun introduction to randomness shows you how to put chaos to work, illustrating its ability to power everything from the simulation of Darwinian evolution, to product placement in a grocery store, to hiding information in plain sight, and even how to generate art and music. By encouraging you to engage in 'what if' speculation, you'll build intuition about when and how to use randomness to get things done. 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. By the end of the book, you'll see why randomness belongs in every programmer's toolbox. Explore the mathematical background of randomness, use randomness for encrypting messages, creating models, and implementing swarm-intelligence or machine-learning algorithms, discover how randomness is used in programming applications, and apply it to your own work.

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.

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, and 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.

If you've been curious about artificial intelligence and 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, 2nd Edition 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 Neighbours, 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. This second edition is thoroughly revised and updated, and adds six new chapters to further your exploration of deep learning from basic CNNs to more advanced models. New chapters cover fine tuning, transfer learning, object detection, semantic segmentation, multilabel classification, self-supervised learning, generative adversarial networks, and large language models. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning, 2nd Edition will give you the skills and confidence to dive into your own machine learning projects.

How Deep Learning Works is for programmers who want to understand deep learning, not just use it. Rather than starting with heavy math or abstract theory, this book takes an experiment-first approach. Each chapter walks readers through carefully designed experiments using real deep learning toolkits, guiding them step by step from running code to understanding why models behave the way they do. A consistent structure-overview, execution, code review, and discussion-keeps readers oriented and focused on building intuition, not memorizing APIs. Beginning with fundamental ideas like classification and neural networks, the book steadily builds toward modern techniques, including transfer learning, zero-shot and few-shot models, and hands-on experiments with large language models. Along the way, readers learn how to spot failure modes, reason about trade-offs, and adapt existing tools to new problems. The result is a clear, software-centric explanation of deep learning that helps working programmers move beyond copy-paste ML and develop real understanding they can apply in their own projects.

Birding with AI introduces readers to the increasingly ubiquitous realm of artificial intelligence and its applications in ornithology and wildlife biology. As well as showcasing the potential utility of deep learning in ornithology, the book demonstrates how to understand, design, implement and evaluate AI models for ornithology and related fields.Readers will learn: - The background of AI, specifically deep learning, and how it applies to image interpretation. - How to build deep-learning models for computer vision and how to compile bird image - About the use of pretrained models, especially CLIP, which alone is capable of out-of-the-box bird detection with high accuracy. - Tailoring CLIP-embedding models with small datasets for specific classification tasks. - How to create models that go beyond classification to localization. - How to classify bird audio recordings. - How to use open source tools like Merlin and BirdNet to augment research-question specific models.This ground-breaking volume adopts an approach based on exploring existing birding tools using AI, leading to an overview of artificial intelligence that will help build intuition about how it works. This provides a foundation for the example projects that follow, enhancing the reader’s confidence in their ability to engage and participate in research involving AI. The projects are designed to guide the reader through the model-building process from dataset creation to training, testing and deployment – whether this be for image recognition, classification of calls or other new frontiers birding.

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

This textbook details the variety of number formats used by computers, thereby helping to ground readers in what can and cannot be represented accurately, especially by floating-point numbers.The book's first part details standard representations of integers and floating-point numbers.  The second explores other number representations, including the wide variety recently developed to support artificial intelligence (AI) and its demand for efficiency in representation to accommodate the ever-expanding scope of neural network models.  Chapters describe each format, with examples in code (Python and C) and exercises.  This new edition includes three new chapters on posits, AI number formats, and a collaborative experiment with an AI to generate novel number formats.Topics and features:Explores how computers use numbers to complete operationsAdds new chapters on posits and AI number formatsIncludes exercises and examples that are code snippets in C or PythonImplements and tests new AI-designed number formats (as designed by GPT-4)Provides thorough grounding on what can and cannot be represented accuratelyA textbook eminently suitable for undergraduates in computer science, the work also will appeal to software developers, engineers, scientists, AI experts, and anyone who programs for fun.

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 details several other number representations. Each chapter ends with exercises to review the key points. Topics covered include interval arithmetic, fixed-point numbers, floating point numbers, big integers and rational arithmetic.This book is for anyone who develops software including software engineerings, 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.