TensorFlow Roadmap

Mastering the TensorFlow by knowing where to start

by Amirsina Torfi

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Book Description

A deep learning is of great interest these days, the crucial necessity for rapid and optimized implementation of the algorithms and designing architectures is the software environment. TensorFlow is designed to facilitate this goal. The strong advantage of TensorFlow is it flexibility is designing highly modular model which also can be a disadvantage too for beginners since lots of the pieces must be considered together for creating the model. This issue has been facilitated as well by developing high-level APIs such as Keras and Slim which gather lots of the design puzzle pieces. The interesting point about TensorFlow is that its trace can be found anywhere these days. Lots of the researchers and developers are using it and its community is growing with the speed of light! So the possible issues can be overcame easily since they might be the issues of lots of other people considering a large number of people involved in TensorFlow community.

MIT License You can download TensorFlow Roadmap ebook for free in PDF format (0.3 MB).

Table of Contents

Chapter 1
Introduction
 
Chapter 1.1
Motivation
 
Chapter 1.2
How to make the most of this effort
 
Chapter 2
Entrance to TensorFlow World
 
Chapter 2.1
Installation
 
Chapter 2.2
Getting Started
 
Chapter 2.3
Going Deeper in TensorFLow
 
Chapter 3
Programming with TensorFlow
 
Chapter 3.1
Reading data and input pipeline
 
Chapter 3.2
Variables
 
Chapter 3.3
TensorFlow Utilities
 
Chapter 4
TensorFlow Tutorials
 
Chapter 4.1
Linear and Logistic Regression
 
Chapter 4.2
Convolutional Neural Networks
 
Chapter 4.3
Recurrent Neural Networks
 
Chapter 4.4
Autoencoders
 
Chapter 4.5
Generative models
 
Chapter 4.6
Multiple GPUs
 
Chapter 5
TensorFlow Projects
 
Chapter 5.1
Comprehensive Tutorials
 
Chapter 5.2
Models
 

Book Details

Subject
Computer Science
Publisher
Self-publishing
Published
2019
Pages
22
Edition
1
Language
English
PDF Size
0.3 MB
License
MIT License

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