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)

Implementing logistic regression

For this recipe, we will implement logistic regression to predict the probability of low birth weight in our sample population.

Getting ready

Logistic regression is a way to turn linear regression into a binary classification. This is accomplished by transforming the linear output into a sigmoid function that scales the output between zero and one. The target is a zero or one, which indicates whether a data point is in one class or another. Since we are predicting a number between zero and one, the prediction is classified into class value 1 if the prediction is above a specified cutoff value, and class 0 otherwise. For the purpose of this example, we will specify that cutoff to be 0.5, which...