2 books in 1 This is the bundle of two successful books in the market

Book 1: In "Deep Learning for Beginners: A comprehensive introduction of deep learning fundamentals for beginners to understanding frameworks, neural networks, large datasets, and creative applications with ease" you will learn:

Deep learning utilizes frameworks which allow people to develop tools which are able to offer better abstraction, along with simplification of hard programming issues. TensorFlow is the most popular tool and is used by corporate giants such as Airbus, Twitter, and even Google.
The book illustrates TensorFlow and Caffe2 as the prime frameworks that are used for development by Google and Facebook. Facebook illustrates Caffe2 as one of the lightweight and modular deep learning frameworks, though TensorFlow is the most popular one, considering it has a lot of popularity, and thus, a big forum, which allows for assistance on main problems.
The book considers several components and tools of deep learning such as the neural networks; CNNs, RNNs, GANs, and auto-encoders. These algorithms create the building blocks which propel deep learning and advance it.
The book also considers several applications, including chatbots and virtual assistants, which have become the main focus for deep learning into the future, as they represent the next frontier in information gathering and connectivity. The Internet of Things is also represented here, as deep learning allows for the integration of various systems via an artificial intelligence system, which is already being used for the home and car functions.
And much more...

Book 2: In "Data Science from Scratch: The #1 Data Science Guide for Everything A Data Scientist Needs to Know: Python, Linear Algebra, Statistics, Coding, Applications, Neural Networks, and Decision Trees" you will learn:

In-depth information about what data science is and why it is important.
The prerequisites you will need to get started in data science.
What it means to be a data scientist.
The roles that hacking and coding play in data science.
The different coding languages that can be used in data science.
Why python is so important.
How to use linear algebra and statistics.
The different applications for data science.
How to work with the data through munging and cleaning.
And much more...

These books provide proven concepts and strategies for people who want to know more about deep learning and how to work with data. After reading this bundle you'll be able to choose the right kind of architecture, how to build a system that can learn, how to train it, and then how to use it to accomplish your goals.

Get your copy of these fantastic guides to master state-of-the-art, deep learning algorithms and their implementation

Deep Learning For Beginners - pocket, Engelska, 2019