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

Understanding generative models


An unsupervised learning model that learns the underlying data distribution of the training set and generates new data that may or may not have variations is commonly known as a generative model. Knowing the true underlying distribution might not always be a possibility, hence the neural network trains on a function that tries to be as close a match as possible to the true distribution.

The most common methods used to train generative models are as follows:

  • Variational autoencoders: A high dimensional input image is encoded by an auto-encoder to create a lower dimensional representation. During this process, it is of the utmost importance to preserve the underlying data distribution. This encoder can only be used to map to the input image using a decoder and cannot introduce any variability to generate similar images. The VAE introduces variability by generating constrained latent vectors that still follow the underlying distribution. Though VAEs help in creating...