Organized by application areas, rather than by specific network architectures or learning algorithms, Building Neural Networks shows why certain networks are more suitable than others for solving specific kinds of problems. Skapura also reviews principles of neural information processing and furnishes an operations summary of the most popular neural-network processing models.

This book is for anyone who wants to understand what neural network[s] are. It's for anyone who wants to make and use their own. And it's for anyone who wants to appreciate the fairly easy but exciting mathematical ideas that are at the core of how they work. This guide is not aimed at experts in mathematics or computer science. You won't need any special knowledge or mathematical ability beyond school maths [sic] ... Teachers can use this guide as a particularly gentle explanation of neural networks and their implementation to enthuse and excite students making their very own learning artificial intelligence with only a few lines of programming language code. The code has been tested to work with a Raspberry Pi, a small inexpensive computer very popular in schools and with young students"--(page 6, Introduction)

Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.

Apply computer vision and machine learning concepts in developing business and industrial applications ​using a practical, step-by-step approach. The book comprises four main sections starting with setting up your programming environment and configuring your computer with all the prerequisites to run the code examples. Section 1 covers the basics of image and video processing with code examples of how to manipulate and extract useful information from the images. You will mainly use OpenCV with Python to work with examples in this section. Section 2 describes machine learning and neural network concepts as applied to computer vision. You will learn different algorithms of the neural network, such as convolutional neural network (CNN), region-based convolutional neural network (R-CNN), and YOLO. In this section, you will also learn how to train, tune, and manage neural networks for computer vision. Section 3 provides step-by-step examples of developing business and industrial applications, such as facial recognition in video surveillance and surface defect detection in manufacturing. The final section is about training neural networks involving a large number of images on cloud infrastructure, such as Amazon AWS, Google Cloud Platform, and Microsoft Azure. It walks you through the process of training distributed neural networks for computer vision on GPU-based cloud infrastructure. By the time you finish reading Building Computer Vision Applications Using Artificial Neural Networks and working through the code examples, you will have developed some real-world use cases of computer vision with deep learning. What You Will Learn · Employ image processing, manipulation, and feature extraction techniques · Work with various deep learning algorithms for computer vision · Train, manage, and tune hyperparameters of CNNs and object detection models, such as R-CNN, SSD, and YOLO · Build neural network models using Keras and TensorFlow · Discover best practices when implementing computer vision applications in business and industry · Train distributed models on GPU-based cloud infrastructure Who This Book Is For Data scientists, analysts, and machine learning and software engineering professionals with Python programming knowledge.

All aboard The Coding Train! This beginner-friendly creative coding tutorial is designed to grow your skills in a fun, hands-on way as you build simulations of real-world phenomena with “The Coding Train” YouTube star Daniel Shiffman. What if you could re-create the awe-inspiring flocking patterns of birds or the hypnotic dance of fireflies—with code? For over a decade, The Nature of Code has empowered countless readers to do just that, bridging the gap between creative expression and programming. This innovative guide by Daniel Shiffman, creator of the beloved Coding Train, welcomes budding and seasoned programmers alike into a world where code meets playful creativity. This JavaScript-based edition of Shiffman’s groundbreaking work gently unfolds the mysteries of the natural world, turning complex topics like genetic algorithms, physics-based simulations, and neural networks into accessible and visually stunning creations. Embark on this extraordinary adventure with projects involving: A physics engine: Simulate the push and pull of gravitational attraction. Flocking birds: Choreograph the mesmerizing dance of a flock. Branching trees: Grow lifelike and organic tree structures. Neural networks: Craft intelligent systems that learn and adapt. Cellular automata: Uncover the magic of self-organizing patterns. Evolutionary algorithms: Play witness to natural selection in your code. Shiffman’s work has transformed thousands of curious minds into creators, breaking down barriers between science, art, and technology, and inviting readers to see code not just as a tool for tasks but as a canvas for boundless creativity. Whether you’re deciphering the elegant patterns of natural phenomena or crafting your own digital ecosystems, Shiffman’s guidance is sure to inform and inspire. The Nature of Code is not just about coding; it’s about looking at the natural world in a new way and letting its wonders inspire your next creation. Dive in and discover the joy of turning code into art—all while mastering coding fundamentals along the way. NOTE: All examples are written with p5.js, a JavaScript library for creative coding, and are available on the book's website.

Take a deep dive into deep learning Deep learning provides the means for discerning patterns in the data that drive online business and social media outlets. Deep Learning for Dummies gives you the information you need to take the mystery out of the topic—and all of the underlying technologies associated with it. In no time, you’ll make sense of those increasingly confusing algorithms, and find a simple and safe environment to experiment with deep learning. The book develops a sense of precisely what deep learning can do at a high level and then provides examples of the major deep learning application types. Includes sample code Provides real-world examples within the approachable text Offers hands-on activities to make learning easier Shows you how to use Deep Learning more effectively with the right tools This book is perfect for those who want to better understand the basis of the underlying technologies that we use each and every day.