Top 7 Free Must-Read Books on Deep Learning ( UPDATED )

Top 7 Free Must-Read Books on Deep Learning

Top 7 Best Free Deep Learning Books You Should be Reading right Now

 

 

Before you pick a Deep learning book, it’s best to evaluate your very own learning style to guarantee you get the most out of the book. 

 

 

You should begin by asking yourself question:

 

How would I best learn? Do I get a kick out of the chance to gain from theoretical writings or Practical examples? Or then again do I jump at the chance to gain from code bits and execution?

 

Everybody has their very own learning style and your answers here will manage which Deep learning books you ought to read.

For me, I get a kick out of the chance to strike a harmony between the two.

Deep learning books that are altogether theoretical and go too far into the unique make it very simple for my eyes to overlook.

However, then again, if a Deep learning book skips theory altogether and hops straight into execution, I know I’m passing up a major opportunity for core issues that may enable me to approach another Deep learning issue or task.

The Deep Learning course reading is an asset planned to enable understudies and specialists to enter the field of machine learning when all is said in done and Deep learning specifically.

 

1.) A Brief Introduction to Neural Networks By D. MICHAEL

 

Neuarl Networks. : A Brief Introduction

Neural networks are a bio-inspired mechanism of data processing, that enables computers to learn technically similar to a brain and even generalize once solutions to enough problem instances are taught.

The manuscript “A Brief Introduction to Neural Networks” is divided into several parts, that are again split to chapters.

 

You can download Soft Copy Here

Hard Cover Available Here

 

 

2.) Deep Learning Deep Learning; Methods and Applications Li Deng and Dong Yu Methods and Applications – By Li Deng and Dong Yu

 

Deep Learning Deep Learning; Methods and Applications Li Deng and Dong Yu Methods and Applications - By Li Deng and Dong Yu

Deep Learning: Methods and Applications is a timely and important book for researchers and students with an interest in deep learning methodology and its applications in signal and information processing.

The application areas are chosen with the following three criteria in mind:

(1) expertise or knowledge of the authors;

(2) the application areas that have already been transformed by the successful use of deep learning technology, such as speech recognition and computer vision; and

(3) the application areas that have the potential to be impacted significantly by deep learning including natural language and text processing, information retrieval, and multimodal information processing empowered by multitask deep learning.

 

You can download Soft Copy Here

You can buy Hard copy of Book Here

 

 

3.) Deep Learning An MIT Press book – By Ian Goodfellow and Yoshua Bengio and Aaron Courville

 

Deep Learning An MIT Press book - By Ian Goodfellow and Yoshua Bengio and Aaron Courville

The online variant of the book is now complete and will stay available online for free.

The deep learning book would now be able to be requested on Amazon. You might want to see Top 20 Amazon Books for Artificial Intelligence & Machine Learning

 

You can download Soft Copy Here

You can buy Hard copy of Book Here

 

 

4.) Neural Networks and Learning Machines Third Edition (New) – By Simon Haykin

 

Neural Networks and Learning Machines Third Edition (New) - By Simon Haykin

 

The new edition has been retitled Neural Networks and Learning Machines, in order to reflect two realities:

1. The perceptron, the multilayer perceptron, self-organizing maps, and neurodynamics, to name a few topics, have always been considered integral parts of neural networks, rooted in ideas inspired by the human brain.

2. Kernel methods, exemplified by support-vector machines and kernel principal components analysis, are rooted in statistical learning theory.

 

You can download Soft Copy Here

You can buy Hard copy of Book here

 

 

5.) FIRST CONTACT WITH TENSORFLOW, get started with Deep Learning programming  By Jordi Torres, Ed. BSC-CNS, Barcelona

 

FIRST CONTACT WITH TENSORFLOW, get started with Deep Learning programming By Jordi Torres, Ed. BSC-CNS, Barcelona

 

This book is oriented to engineers with only some basic understanding of Machine Learning who want to expand their wisdom in the exciting world of Deep Learning with a hands-on approach that uses TensorFlow.

 

You can download Soft Copy Here

You can buy Hard copy of Book here

 

 

6.) Neural Networks and Deep Learning By Michael Nielsen

 

Neural Networks and Deep Learning - Michael Nielsen

 

Neural Networks and Deep Learning is a free online book. The book will teach you about:

  • Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data
  • Deep learning, a powerful set of techniques for learning in neural networks

Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing.

 

You can download Soft Copy Here

You can buy Hard copy of Book here

 

 

7.) Neural Network Design (2nd Edition) – By Martin T. Hagan, Howard B. Demuth, Mark H. Beale, Orlando De Jes

 

Neural Network Design (2nd Edition) - By Martin T. Hagan, Howard B. Demuth, Mark H. Beale, Orlando De Jes

This book provides a clear and detailed survey of fundamental neural network architectures and learning rules. In it, the authors emphasize a fundamental understanding of the principal neural networks and the methods for training them.

The authors also discuss applications of networks to practical engineering problems in pattern recognition, clustering, signal processing, and control systems. Readability and natural flow of material is emphasized throughout the text.

Key Features

  • Extensive coverage of performance learning, including the Widrow-Hoff rule, backpropagation and several enhancements of backpropagation, such as the conjugate gradient and Levenberg-Marquardt variations.
  • Both feedforward network (including multilayer and radial basis networks) and recurrent network training are covered in detail. The text also covers Bayesian regularization and early stopping training methods, which ensure network generalization ability.
  • Associative and competitive networks, including feature maps and learning vector quantization, are explained with simple building blocks.
  • A chapter of practical training tips for function approximation, pattern recognition, clustering and prediction applications is included, along with five chapters presenting detailed real-world case studies.
  • Detailed examples, numerous solved problems and comprehensive demonstration software.

 

You can download Soft Copy Here

You can buy Hard copy of Book here

 

 

FINAL WORDS

 

Start Understanding these books One by One. Perhaps, a 10-15$ book can change your Career for eternity.

Books changed my life in recent years. I was forced to peruse books in my formal education and avoided from perusing books in my corporate activity.

 

 

Related:

Top 20 Amazon Books for Artificial Intelligence & Machine Learning 

PS: The amazon links in this article are affiliate links. On the off chance that you purchase a book through this connection, we would get paid through Amazon. This is one of the routes for us to take care of our expenses while we keep on creating these amazing articles. Further, the list reflects our suggestion in light of substance of book and is no chance impacted by the commission.

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