Neural network computing is the latest thing to hit the software industry, this computer system is inspired by the basic framework of biological neural networks. Unlike conventional computing, where the software is given task-specific rules and guidelines, neural networks are rather given examples through which it will “learn” to perform tasks. An artificial neural network has three parts, these three parts are input, hidden, and output. Artificial neural networks are now being used for speech recognition, playing board and video games, medical diagnosis, machine translation, social network filtering, and computer vision. Artificial neural networks are slowly growing to be the future of computing and AI, thus it is crucial that you know it to stay on top of the industry. Here are the top books you can use to learn neural networks. The list of books below can help anyone from a beginner to advanced users who want to improve their understanding.
Neural Networks with Keras Cookbook: Over 70 recipes leveraging deep learning techniques across image, text, audio, and game bots
- Build multiple simple and advanced neural network architectures from the ground up Knowing how to transfer gained knowledge to detect objects in images and classify them Building a program for a self-driving car using a convolutional neural network. Knowing how a system is able to code for images, texts, and recommender computer systems. Building AIs to play games Teaching computers to analyze text
This book is written by V Kishore Ayyadevara, he is an expert in the field of AI and has been using it to solve problems in the healthcare sector along with his team. He has been in the field of data science for over ten years. This book of his teaches you everything from the basics of neural networks to advance the implementation of neural network architectures is a simple recipe-like explanation. The various things that you will learn are
This book is aimed at beginners and intermediate level machine learning developers. All this book requires you to have a solid foundation in Python programming and a basic understanding of machine learning. While this book is not cheap, it will teach you neural networks in a simple manner.
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This book is written by Tariq Rashid. This book aims to teach you the mathematics behind neural systems and using Python to make your own artificial neural system. The neural system enables deep learning and AI, these two computer systems have been performing many amazing feats in the past few years. Even though these are the future of computer science, not many people truly understand them. This book aims to teach beginners, starting from the basics of neural networks and slowly teaching them how a neural network actually works. While this book does teach mathematics, it does not require the reader to have more than secondary school mathematics knowledge. There is even an introduction to calculus attached to help you brush up your basics. This book will teach you to program using python and make your own artificial neural network. It will even let you develop AIs that can recognize handwritten numbers, while also discussing more advanced models and uses. The book has three parts, the first part deals with teaching you the mathematics behind neural networks in a simple way. The second part deals with the practicality of neural networks, you will learn to program neural networks in Python and slowly improve them to reach industry standards. The third part builds on the second part and greatly improves your program, it also lets you test the program out with your handwriting. All the codes in this book will run in Raspberry Pi Zero. This book is best for beginners who want to gain an insight into the intriguing world of AI.
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This book is written by Charu C. Aggarwal. This book will teach you the algorithms and theories of deep learning. Understanding the theory and algorithms of deep learning is crucial to know how each design concept of neural network architectures is applied in various applications. This book covers everything from the classical models of deep learning to the latest models. This book is primarily meant for graduate students, practitioners, and researchers. The book even has exercises with solutions. This book is a little expensive.
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This book is written by Ian Goodfellow, Yoshua Bengio, and Yoshua Bengio. These three people are leading experts in the field of deep learning. This book serves as an introduction to the extremely broad range of topics in deep learning, it covers everything from theoretical and mathematical concepts of deep learning to the neural network techniques used by industries. This book covers optimization algorithms, convolutional networks, sequence modeling, deep feedforward networks, and regularization. This book is one of the cheapest books you’ll find on the subject. This book is ideal for undergraduate or postgraduate students who are planning on a future in research about this topic. It is also useful for software engineers who want to integrate deep learning into their applications. This book has been praised by Elon Musk, Yann LeCun, and Geoffrey Hinton FRS.
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