Bøger af Tariq Rashid

A step-by-step gentle journey through the mathematics of neural networks, and making your own using the Python computer language. Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included. The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already! You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks. Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples. Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals. Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi. All the code in this has been tested to work on a Raspberry Pi Zero.

A gentle introduction to Generative Adversarial Networks, and a practical step-by-step tutorial on making your own with PyTorch. This beginner-friendly guide will give you hands-on experience: learning PyTorch basicsdeveloping your first PyTorch neural networkexploring neural network refinements to improve performanceintroduce CUDA GPU accelerationIt will introduce GANs, one of the most exciting areas of machine learning: introducing the concept step-by-step, in plain Englishcoding the simplest GAN to develop a good workflowgrowing our confidence with an MNIST GANprogressing to develop a GAN to generate full-colour human facesexperiencing how GANs fail, exploring remedies and improving GAN performance and stabilityBeyond the very basics, readers can explore more sophisticated GANs: convolutional GANs for generated higher quality imagesconditional GANs for generated images of a desired classThe appendices will be useful for students of machine learning as they explain themes often skipped over in many courses: calculating ideal loss values for balanced GANsprobability distributions and sampling them to create imagescarefully chosen examples illustrating how convolutions worka brief explanation of why gradient descent isn't suited to adversarial machine learningAll code is available publicly as open source on github.

A Gentle Introduction to Creative Coding with P5js.A fun step-by-step gentle introduction to creating digital art with computers, designed especially for: artists new to coding art, design and digital media students, technologists wanted to explore their creativity teachers and parents seeking more visual and exciting approaches to teaching computer science Starting from the very basics, we'll learn to: understand how computers create digital images code with a popular computer language designed for artists, called Processing, enabled for the web with p5js develop and appreciate algorithms, mathematical recipes, which can create surprisingly beautiful art easily share your code and art on the web, potentially reaching an audience of billions of internet users We'll discover and practice basic computer graphics techniques, explore simple algorithms that create interesting visual forms, and work through example projects to experience the process of developing algorithmic art from inspiration, through problem solving, to final refinement. By the end of the course, you will be coding confidently, appreciating the beauty of mathematics and wanting to explore more advanced ideas and methods.