Book Image

TensorFlow Machine Learning Cookbook - Second Edition

By : Nick McClure
Book Image

TensorFlow Machine Learning Cookbook - Second Edition

By: Nick McClure

Overview of this book

TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and allow you to dig deeper and gain more insights into your data than ever before. With the help of this book, you will work with recipes for training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and more. You will explore RNNs, CNNs, GANs, reinforcement learning, and capsule networks, each using Google's machine learning library, TensorFlow. Through real-world examples, you will get hands-on experience with linear regression techniques with TensorFlow. Once you are familiar and comfortable with the TensorFlow ecosystem, you will be shown how to take it to production. By the end of the book, you will be proficient in the field of machine intelligence using TensorFlow. You will also have good insight into deep learning and be capable of implementing machine learning algorithms in real-world scenarios.
Table of Contents (13 chapters)

Using a multilayer neural network

We will now apply our knowledge of different layers to real data by using a multilayer neural network on the low birth weight dataset.

Getting ready

Now that we know how to create neural networks and work with layers, we will apply this methodology with the aim of predicting the birth weight in the low birth weight dataset. We'll create a neural network with three hidden layers. The low birth weight dataset includes the actual birth weight and an indicator variable for whether the birth weight is above or below 2,500 grams. In this example, we'll make the target the actual birth weight (regression) and then see what the accuracy is on the classification at the end. At the end, our...