A.I. & Optimization

Advanced Machine Learning, Data Mining, and Online Advertising Services

Top 10 Deep Learning and Neural Networks Books

The AI Optify data team writes about topics that we think machine learning enthusiasts will love. AI Optify has affiliate partnerships so we may get a share of the revenue from your purchase.

Top Deep Learning & Neural Networks Books - For this post, we have scraped various signals (e.g. online reviews/ratings, covered topics, author influence in the field, year of publication, social media mentions etc.) from web for more than 30's Deep Learning & Neural Networks books. We have fed all above signals to a trained Machine Learning algorithm to compute a score for each book and rank the top books.

The readers will love our list because it is Data-Driven & Objective. Enjoy the list:

1. Make Your Own Neural Network

Quality Score: 100/100

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.

2. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Quality Score: 100/100

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

3. Neural Network Design (2nd Edition)

Quality Score: 84/100

This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules. In it, the authors emphasize a coherent presentation of the principal neural networks, methods for training them and their applications to practical problems. Features Extensive coverage of training methods for both feedforward networks (including multilayer and radial basis networks) and recurrent networks.

4. Neural Networks for Pattern Recognition (Advanced Texts in Econometrics)

Quality Score: 73/100

This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modeling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications.

5. Neural Networks: A Comprehensive Foundation (2nd Edition)

Quality Score: 67/100

For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Thoroughly revised.

6. Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms

Quality Score: 62/100

With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research that is paving the way for modern machine learning. This book uses exposition and examples to help you understand major concepts in this complicated field. Deep learning is still a pretty complex and difficult subject to grasp. If you have a basic understanding of what machine learning is, have familiarity with the Python programming language, and have some mathematical background with calculus, this book will help you get started.

7. Deep Learning (Adaptive Computation and Machine Learning series)

Quality Score: 67/100

The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.

8. Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks (MIT Press)

Quality Score: 62/100

Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. The basic idea is that massive systems of simple units linked together in appropriate ways can generate many complex and interesting behaviors. This book focuses on the subset of feedforward artificial neural networks called multilayer perceptrons (MLP). These are the mostly widely used neural networks, with applications as diverse as finance (forecasting), manufacturing (process control), and science (speech and image recognition).

9. Fundamentals of Artificial Neural Networks (MIT Press)

Quality Score: 45/100

As book review editor of the IEEE Transactions on Neural Networks, Mohamad Hassoun has had the opportunity to assess the multitude of books on artificial neural networks that have appeared in recent years. Now, in Fundamentals of Artificial Neural Networks, he provides the first systematic account of artificial neural network paradigms by identifying clearly the fundamental concepts and major methodologies underlying most of the current theory and practice employed by neural network researchers. Such a systematic and unified treatment, although sadly lacking in most recent texts on neural networks, makes the subject more accessible to students and practitioners.

10. Deep Learning: A Practitioner's Approach

Quality Score: 28/100

Looking for one central source where you can learn key findings on machine learning? Deep Learning: A Practitioner's Approach provides developers and data scientists with the most practical information available on the subject, including deep learning theory, best practices, and use cases. Authors Adam Gibson and Josh Patterson present the latest relevant papers and techniques in a non­academic manner, and implement the core mathematics in their DL4J library. If you work in the embedded, desktop, and big data/Hadoop spaces and really want to understand deep learning, this is your book.