Book Image

TensorFlow Machine Learning Projects

By : Ankit Jain, Amita Kapoor
Book Image

TensorFlow Machine Learning Projects

By: Ankit Jain, Amita Kapoor

Overview of this book

TensorFlow has transformed the way machine learning is perceived. TensorFlow Machine Learning Projects teaches you how to exploit the benefits—simplicity, efficiency, and flexibility—of using TensorFlow in various real-world projects. With the help of this book, you’ll not only learn how to build advanced projects using different datasets but also be able to tackle common challenges using a range of libraries from the TensorFlow ecosystem. To start with, you’ll get to grips with using TensorFlow for machine learning projects; you’ll explore a wide range of projects using TensorForest and TensorBoard for detecting exoplanets, TensorFlow.js for sentiment analysis, and TensorFlow Lite for digit classification. As you make your way through the book, you’ll build projects in various real-world domains, incorporating natural language processing (NLP), the Gaussian process, autoencoders, recommender systems, and Bayesian neural networks, along with trending areas such as Generative Adversarial Networks (GANs), capsule networks, and reinforcement learning. You’ll learn how to use the TensorFlow on Spark API and GPU-accelerated computing with TensorFlow to detect objects, followed by how to train and develop a recurrent neural network (RNN) model to generate book scripts. By the end of this book, you’ll have gained the required expertise to build full-fledged machine learning projects at work.
Table of Contents (23 chapters)
Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
Index

Summary


In this chapter, we first looked at what GANs are. They are a new kind of generative model that helps us to generate new images. 

We also touched upon other kinds of generative models, such as Variational Auto-encoders and PixelRNN, to get an overview of different kinds of generative models. We also talked about different kinds of GANs to discuss the progress that had been made in this space since the first paper on GANs was published in 2014.

Then, we learned about DiscoGANs, a new type of GAN that can help us to learn about cross- domain relationships. Specifically, in this chapter, our focus was on building a model to generate handbag images from shoes and vice versa.

Finally, we learned about the architecture of DiscoGANs and how they differ from usual GANs.

In the next chapter, we will learn how to implement capsule networks on the Fashion MNIST dataset.