Top 7 books to learn deep learning

Top 7 books to learn deep learning BestBooksAZ's mission to to provide best books list to your life and career. We hope that you love ...

Top 7 books to learn deep learning

BestBooksAZ's mission to to provide best books list to your life and career. We hope that you love this list. If you have any questions, pls leave your comment below.

1. Deep Learning

It is quite hard to create a post that relates to the book s of the deep learning without even mentioning texts like Bengjo, Courville or Goodfellow of deep learning.

This specific book is intended to be like a textbook that can be used to learn fundamental from and as well as theory which surrounds the concepts of deep learning at a level of college.

Deep Learning of Goodfellow is completely theoretical and specifically intended to entertain the audience related to the academics. There is not a single code in this book regarding Deep Learning.
This textbook initiates with describing the basics of machine learning which also includes the applied mathematics in it which is needed for studies to be effective of deep learning. This book may include topics such as probability, linear algebra, and information theory, etc. They are all related to deep learning and is according to the perspective of academics as well.

From that point, this book shifts to the concepts of the latest deep learning which includes different techniques and as well as algorithms.

The last part of this deep learning is to put the focus on the present research trends as well as where this field’s moving.

This book is quite useful and I have read it myself more than once and found it quite helpful that is if you have the academic rigor of such an amazing book.

This book is also available online which is free as well and you can get it from the book’s homepage.
But, if you want to get a printed copy of it, you can get it from Amazon as well.

You should give this book a try if
• You are into the theory and not in implementation.
• You like to do academics related writing
• Good to use for undergraduates, graduates and as well as for professor who is working in the deep learning.

2. Deep Learning and Neural Networks

The second book which is also based on deep learning is of the author, Michael Nielsen, and the book is Neural Networks and Deep Learning.

It includes sample code as it is important and there are around seven scripts of python. All of these are associated with a basic neural network, machine learning and deep learning concepts which are based on the MNIST dataset. Some of these implementations are not that of utmost importance but they do help in demonstrating a few of the concepts in the text.

Especially if you’re a beginner in this field and ie eager as well to dive into it that this book will be quite good for you.

This book is also easy to read as compared to the book of Goodfellow and Nielsen’s as well.

You should give this book a try if:
• You are into the theory and looking to dive deeper into it.
• You are a beginner in this course.

3. Deep Learning with Python

An AI researcher of Google and also a creator of a popular Keras deep learning library published the book, Deep Learning with Python, which is of Francois Chollet in October 2017.

This book takes some practitioner’s approach associated with deep learning. Also, some brief discussion is also included, but very few paragraphs associated with the theory, you will also be able to find the implementation of Keras.

The most likable aspect of this specific book is how the author involves some examples associated with the deep leaning that is being applied to the computer vision, and as well as to the text along with sequences which can be made well for the readers who are in to this KEras library and are eager to learn it.

The writing is quite clear and as well as accessible. Some commentary on the trends of deep leaning is somewhat insightful and phenomenal.

IT is fundamental that this specific book is not to deep dive into deep learning. In fact, the main use of it is to make you learn about the basics of the concept of deep learning by using the library of Keras along with some practical examples.

You should give this book a try if:
• You are eager to learn about Keras library.
• You want to learn through implementation.
• You are eager to learn about the latest concepts about deep learning.

4. Hands-On Machine Learning with Scikit-Learn and TensorFlow

The title was considered quite vague, but thanks to the word TensorFlow included in the book, Hands-on Machine Learning with Scikit-Learn and TensorFlow, which provided a concept that it may include more than just basics of machine learning.

At the very same moment, TensorFlow seems to be the focus of this book as this word represents this. It may sound like that it may be some cheap trick to make people buy it.

But this was not the case. This book is actually quite good.

This book is divided into two parts.

The first is associated with the basics of machine learning. Some algorithms are Decision Trees, Ensemble Methods, Random Forests, Support Vector Machines, and some unsupervised algorithms as well.

The second is associated with the elementary concepts which will be through the use of library fo TensorFlow.

You should give this book a try if:
• You want to initiate it with some core concepts.
• You are into a popular library of Scikit – Learn.
• You are eager to learn about the TensorFlow to operate it.

5. TensorFlow Deep Learning Cookbook

If you are into cookbook kind of style than this book, TensorFlow Deep Learning Cookbook, is quite good or you.

This book is also a nice reference for those who use TensorFlow. Also, this specific book does not mean to teach about deep leaning. In fact, this is for you to get to know about how you can operate TensorFlow related to deep learning.

You will still learn about new concepts and techniques though. But this book uses a cookbook approach which inc=volves lots of heavy code and as well as the explanations about the code.
The only critic can be quite as some typos are in the snippets of the code. This is an expected outcome, typos do occur. You will just have to be careful around it.

You should this book a try:

• You already know about the basics.
• You are eager to learn about the library of TensorFlow.
• You like the cookbook, a kind of guide where code be already provided to work with.

6. Deep Learning: A Practitioners Approach

Many of the books associated with deep learning include samples of code that use python. Deep Learning: A Practitioners Approach of Josh Patterson and Adam Gibson does not follow it though. They use java instead and as well as also use DL4J library. You may be wondering about java, why?
Java is one of those programming languages who is used quite a lot in many large corporations. This is usually the case in enterprises.

The starting chapters of this amazing book describes the basic concepts of deep learning and as well as of machine learning. The remaining part of this book based on the sample codes which is based on Java and is using DL4J library.

You should try this book that is if:

• You need a specific book where java can be utilized in this scenario.
• You are working for a big enterprise where java’s mandatory.
• You need to have an understanding of DL4J.

7. Deep Learning for Computer Vision with Python

This portion is quite biased if I am, to be honest with you. I have written this book on this topic “Deep Learning for Computer Vision with Python”.

Being said that, this book of mine is really the best when it comes to deep learning along with the resources related to the computer vision.

A certain researcher named Francois Chollet who is the researcher of an AI at the amazing company Google and at the Keras’s creator as well. This had to be said about this new of mine which is that the book is quite great and have great insight into it and into the deep learning as well for the computer vision practically. This is quite a good approach and also enjoyable as well to read. Explanations are quite clear and as well as contains good details. You will be able to find a lot of practical tips as well and will find some recommendations which are rarely found in books or among the courses at your university. I will highly recommend you this book. Not only do you but also to all the beginners and as well as to the practitioners as well.

And one of the authors, Adam Geitgey, of Machine Learning, is Fun! Said that he highly recommended this amazing book related to deep learning with python for the computer vision. This is the one book that you can use to cover how things will work, how you can actually use them in your work in this real world to resolve extremely challenging problems.

Especially if you are into deep learning which can be applied to the computer vision which includes image classification, image understanding, object detection, etc. then this will be the book which is for you to use.

By using this book, you can learn the following things:

• Know about the basic concepts of the machine learning, of deep learning as well in quite a nice manner that provides a balance between theory and as well as in implementation.

• Know about the advanced techniques of the deep learning which includes multi-GPU training, object detection, GANS [Generative Adversarial Networks] and as well as the transfer learning.
• The outcomes of the papers of state-of-the-art to replicate it which also includes SqueezNet, VGGNet, ResNet and some other among the ImageNet dataset consisting of 1.2 million.

Not only this, the best balance which is possible is provided to the theory and to the implementation. For every theoretical concept, you will see in this book, you will also find an algorithm associated with it which will be based on python to support you in your study.

Be sure to take a look — during checking this book out, make sure you go through the sample chapters as well and as well as the contents too.

You should use this book that is if:

• You are into the computer vision as well as into image understanding associated with the deep learning.
• You need exceptional stability between the theoretical model and execution
• You need a book that will make you understand complicated algorithms quite easily.
• You need a book that can guide you easily while learning about this amazing deep learning.


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