Book Image

Machine Learning Using TensorFlow Cookbook

By : Luca Massaron, Alexia Audevart, Konrad Banachewicz
Book Image

Machine Learning Using TensorFlow Cookbook

By: Luca Massaron, Alexia Audevart, Konrad Banachewicz

Overview of this book

The independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data. Dive into recipes on training models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google’s machine learning library, TensorFlow. This cookbook covers the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You’ll discover real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and regression. Explore the practical applications of a variety of deep learning architectures, such as recurrent neural networks and Transformers, and see how they can be used to solve computer vision and natural language processing (NLP) problems. With the help of this book, you will be proficient in using TensorFlow, understand deep learning from the basics, and be able to implement machine learning algorithms in real-world scenarios.
Table of Contents (15 chapters)
5
Boosted Trees
11
Reinforcement Learning with TensorFlow and TF-Agents
13
Other Books You May Enjoy
14
Index

Implementing logistic regression

For this recipe, we will implement logistic regression to predict the probability of breast cancer using the Breast Cancer Wisconsin dataset (https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)). We will be predicting the diagnosis from features that are computed from a digitized image of a fine needle aspiration (FNA) of a breast mass. An FNA is a common breast cancer test, consisting of a small tissue biopsy that can be examined under a microscope.

The dataset can immediately be used for a classification model, without further transformations, since the target variable consists of 357 benign cases and 212 malignant ones. The two classes do not have the exact same consistency (an important requirement when doing binary classification with regression models), but they are not extremely different, allowing us to build a straightforward example and evaluate it using plain accuracy.

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