TensorFlow, that is the best way to build deep learning models, is an open source software library for numerical computation using stateful data flow graphs. In other Words, It is an "An open-source software library for Machine Intelligence". When it was open-sourced, it was made to be efficient, flexible, extensible, and portable.
In this Article, We will discuss various Hands-On Books related to Tensorflow, that give you a detailed view and an understanding of the open-source machine learning Library:
1. Machine Learning with TensorFlow:
This must-read book by Nishant Shukla who is a computer vision researcher at UCLA aims at teaching its readers about machine learning algorithms and how to implement the solutions using TensorFlow. The book starts with an overview of concepts related to machine learning and then slowly moves on to the essentials that are needed to begin using TensorFlow. Each chapter of the book deeply looks into a prominent example of machine learning.
Accordingly, Readers can cover all of them if they aim at mastering the basics or skip around as they wish to cater to their needs. By the end of this book, readers will be eligible to solve clustering, classification, regression, and prediction problems in the real world.
This book is basically is aimed at programmers who have some knowledge with Python and linear algebra concepts like vectors and matrices and doesn't necessarily require any prior experience with machine learning.
Github: Machine Learning with TensorFlow
2. First Contact with TensorFlow:
A book by Jordi Torres who is currently a professor at UPC Barcelona Tech comes with a purpose to help to spread knowledge related to TensorFlow among engineers who aim at expanding their wisdom in the world of machine learning. "We believe that anyone with an engineering background might require from now on Deep Learning, and Machine Learning in general, to apply it in their work."
As the title of the book indicates, it is the first contact with TensorFlow in order to help its users to get started with Deep Learning programming. The book requires its reader to have some basic understanding of machine learning and has a practical nature that in return reduces the theoretical part as much as possible.
More Information: First Contact with TensorFlow
3. Deep Learning with Python:
It is a book by Jason Brownlee that is written in the friendly Machine Learning Mastery style that the readers are used to.
It helps the readers to learn exactly how to get started with deep learning and apply the same to various machine learning projects as well. In Short, it helps Develop Deep Learning Models on Theano and TensorFlow Using Keras.
More Information: Deep Learning with Python
4. TensorFlow for Machine Intelligence:
This book comes with a hands-on introduction to learning algorithms. It is aimed at those people who have a little knowledge of machine learning or not at all and who have heard about TensorFlow but found the documentation too daunting to approach.
The learning curve of the book is gentle enough as this book starts with the absolute basics of TensorFlow and you always have some code to illustrate the math with a step-by-step approach to the same.
5. Getting Started with TensorFlow:
A book by Giancarlo Zaccone comes with an aim to help its readers "Get up and running with the latest numerical computing library by Google and dive deeper into your data! "
As you progress through the book, with the help of practical examples you'll learn to implement various machine learning techniques such as classification, neural networks, clustering, and deep learning and By the end of this book, will have gained hands-on experience of not only using TensorFlow but also building classification, language processing, image recognition systems, and information retrieving systems for your application.
6. Hands-On Machine Learning with Scikit-Learn and TensorFlow:
A practical book by Aurélien Geron, helps the readers in gaining an intuitive to understand the concepts and tools required for building intelligent systems and also Covers Machine learning fundamentals, the latest CNN, RNN and Autoencoder architectures, and Reinforcement Learning (Deep Q), training and deploying deep nets across multiple servers and GPUs using TensorFlow.
More Information: Hands-On Machine Learning with Scikit-Learn and TensorFlow
7. Building Machine Learning Projects with Tensorflow:
This is an example-rich, practical and methodically explained guide by Rodolfo Bonnin that teaches its audience on how to perform highly accurate and efficient numerical computing with TensorFlow and also allows you to apply the features of Tensorflow from the very start.
The above-listed books are only a few selected books that provide its readers with the knowledge that they seek. With variations in each book, the audience can go with the right guide, that leads them the way at what they aim.