Introduction to Tensorflow – Ebook

Numerous big & small companies are considering artificial intelligence & machine learning as an important field for their businesses. It is mostly because of the fact that ML promises lots of possibilities in almost all the domains, and these companies want to be competitive in this digital age. Because of this, a lot of individuals are showing their interests to learn different tools for machine learning. Considering this, we have curated an exclusive Ebook completely revolving around TensorFlow which is one of the most popular and widely used tools or libraries for machine learning applications.

Why should I choose this Ebook?

It will help you in understanding both basics and the advanced concepts of TensorFlow along with the codes. It will make you capable enough to easily work with it to build various machine learning applications. This Ebook covers a vast range of concepts related to TensorFlow along with the realworld project for giving you a more clear picture.

What makes this Ebook so valuable?

This Ebook on TensorFlow consists of all the required topics for the better understanding of TensorFlow along with Generative Adversarial Network. Initially, it focuses on the basic introduction, deep learning, TensorFlow 2.0, data analysis & neural networks. Moreover, it describes different types of autoencoders, GANs and other crucial aspects related to TensorFlow. Moreover, this Ebook also includes a live project of “Toxic Comment Classification Challenge” in TensorFlow.

This Ebook includes

1.A basic introduction to TensorFlow, TensorBoard, the role of Python, components & APIs of TensorFlow

2.Deep Learning

 Artificial Neural Network, Recurrent Neural Network & Convolutional Neural Network

3.TensorFlow 2.0

 Basics, its importance, variables, placeholders, installation, environment setup, TensorFlow graphs

4.Machine Learning Basics

 Essential packages, implementation, Scikit learn, classification & regression models & data visualization

5.Neural Network

 Basics, perceptrons, different activation functions, cost functions, gradient descent & backpropagation

6.Autoencoders

 Different types, dimensionality reduction with linear autoencoder, stacked autoencoder

7.Generative Adversarial Networks

 Introductions, deployment, applications & code

8.Live project in TensorFlow

 Toxic comment classification challenge

Get started with this Ebook now to learn different concepts related to TensorFlow & GANs for acing or building advanced machine learning applications!