[FREE] Introduction to Tensorflow - Ebook 2020
Learn the essentials of Tensorflow from ground up, Get started with this comprehensive E-Book on Tensorflow and GANs to learn and create Machine Learning models. Enroll for this online TensorFlow E-BookInstructor: Eduonix Learning Solutions
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About this Course
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.
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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!
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