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


Neural networks, as we know, are great for point predictions, but can't help us identify the uncertainty in their predictions. On the other hand, Bayesian learning is great for quantifying uncertainty, but doesn't scale well in multiple dimensions or problems with big unstructured datasets such as images.

In this chapter, we looked at how we can combine neural networks with Bayesian learning using Bayesian neural networks.

We used the dataset of German Traffic Signs to develop a Bayesian neural network classifier using Google's recently released tool: TensorFlow probability. TF probability provides high-level APIs and functions to perform Bayesian modeling and inference.

We trained the Lenet model on the dataset. Finally, we used Monte Carlo to sample from the posterior of the parameters of the network to obtain predictions for each sample of the test dataset to quantify uncertainty.

However, we have only scratched the surface in terms of the complexity of Bayesian neural networks. If...