Best Books for Learning Artificial Neural Networks
Posted on 09-25-2017 by
A neural network is a powerful computational data model that is able to capture and represent complex input/output relationships. The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform "intelligent" tasks similar to those performed by the human brain. To deep Learning on neural networks here we make a list of best seller and good books review.
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!
What You Will Learn
- Introduce the mathematical ideas underlying the neural networks
- You'll learn to code in Python and make your own neural network
- Teaching it to recognise human handwritten numbers
- Gradually builds up a neural network which can learn to recognise human handwritten numbers
- Take a privileged peek inside the mysterious mind of a neural network.
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.
What You Will Learn
- Including feature maps and learning vector quantization
- Tips for function approximation, pattern recognition
- Clustering and prediction
- In addition to conjugate gradient and Levenberg-Marquardt variations of the backpropagation algorithm
- Detailed examples and numerous solved problems.
From smart bulbs to self-driving cars, intelligent machines are becoming ever more prevalent in our day to day lives. The underpinning of this technology is called machine learning, and is the same basic concept that is used by marketing experts to target ads on webpages and collect data about their customers. The uses for machine learning in today’s world are vast and ever expanding. The technology is poised to revolutionize the way people interact with machines on a daily basis. Understanding just how these programs and processes function can help you to navigate this new technology.
What You Will Learn
- Just what machine learning is and why it’s important
- Supervised versus unsupervised algorithms and the potential uses of each
- Description of some of the most popular machine learning algorithms
- The role of machine learning in programs like Cortana, Alexa, Siri, or Google assist.
- Explain current neural network technologies
- How to including ReLU activation
- Stochastic gradient descent
- Cross-entropy, regularization
- Dropout, and visualization.
Networks are involved in many aspects of everyday life, from food webs in ecology and the spread of pandemics to social networking and public transportation. In fact, some of the most important and familiar natural systems and social phenomena are based on a networked structure. It is impossible to understand the spread of an epidemic, a computer virus, large-scale blackouts, or massive extinctions without taking into account the network structure that underlies all these phenomena.
What You Will Learn
- Guido Caldarelli and Michele Catanzaro discuss the nature and variety of networks
- Using everyday examples from society, technology, nature, and history to illuminate the science of network theory
- Describe the ubiquitous role of networks
- Discuss how networks can spontaneously collapse.
- Highlighting how the findings of complex network theory have very widely
- Important applications in genetics, ecology, communications, economics, and sociology.
Neural Networks and Learning Machines, Third Edition is 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. This is ideal for professional engineers and research scientists.
Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.
A step-by-step visual journey through the mathematics of neural networks, and making your own using Python and Tensorflow. Once we dig a bit deeper though, we discover that a handful of mathematical functions play a major role in the trial and error process. It also becomes clear that a grasp of the underlying mathematics helps clarify how a network learns. You will learn to build a simple neural network using all the concepts and functions we learned in the previous few chapters. Our example will be basic but hopefully very intuitive. Many examples available online are either hopelessly abstract or make use of the same data sets, which can be repetitive. Our goal is to be crystal clear and engaging, but with a touch of fun and uniqueness. This section contains the following eight chapters.
What you will gain from this book
- A deep understanding of how a Neural Network works
- How to build a Neural Network from scratch using Python
- Forward Propagation
- Calculating The Total Error
- Calculating The Gradients
- Updating The Weights.
Deep learning has been a great part of various scientific fields and since this is my third book regarding this topic, you already know the great significance of deep learning in comparison to traditional methods. At this point, you are also familiar with types of neural networks and their wide range of applications including image and speech recognition, natural language processing, video game development and other. Deep learning is pretty complex subject, but since you already have a fundamental knowledge of this topic, getting to know convolutional neural networks better is next logical step.
What you will learn
- Architecture of convolutional neural networks
- Solving computer vision tasks using convolutional neural networks
- Python and computer vision
- Automatic image and speech recognition
- Theano and TenroeFlow image recognition
- How to use MNIST vision dataset
- What are commonly used convolutional filters.
The best part about this book is that it doesn’t require a college degree. Your high school math skills are quite enough for you to get a good grasp of the basics and learn how to build an artificial neural network. From non-mathematical explanations to teaching you the basic math behind the ANNs and training you how to actually program one, this book is the most helpful guide you will ever find. Carefully designed for you, the beginner, this guide will help you become a proud owner of a neural network in no time.
What You Will Learn
- The 6 unique benefits of neural networks
- The difference between biological and artificial neural networks
- And inside look into ANN (Artificial Neural Networks)
- The industries ANN is used in
- How to teach neural networks to perform specific commands
- The different types of learning modalities (e.g. Hebbian Learning, unsupervised learning, supervised learning etc.)
- The architecture of ANN
- Basic math behind artificial neurons
- Simple networks for pattern classification
- The Hebb Rule
- How to build a simple neural network code
- The backpropogation algorithm and how to program it
- And much, much more!
Neural networks is a book that provides a solid basis for simple neural network concepts. It incorporates a top level view of neural network architectures for practical information analysis done with the aid of extensive step-by-step analysis on linear networks, in addition to, multi-layer perceptron for nonlinear prediction and classification enlightening all stages of processing and model development explained through useful examples and case studies. It is ideal for both beginners and advanced readers alike.
What You Will Lean
- It gives an in depth coverage on Self organization for nonlinear information clustering
- Recurrent networks for linear nonlinear time series forecasting
- Different community kinds appropriate for scientific record analysis
- You will also be able to make your own neural network
- Teaching it to recognize human handwritten numbers, and performing as well as professionally developed networks.