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Python Machine Learning

You're reading from Python Machine Learning Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2

Product type Paperback

Published in Dec 2019

Publisher Packt

ISBN-13 9781789955750

Length 772 pages

Edition 3rd Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Authors (2):

Vahid Mirjalili

Sebastian Raschka

View More author details

Table of Contents (21) Chapters

Preface

1. Giving Computers the Ability to Learn from Data

2. Training Simple Machine Learning Algorithms for Classification FREE CHAPTER

3. A Tour of Machine Learning Classifiers Using scikit-learn

4. Building Good Training Datasets – Data Preprocessing

5. Compressing Data via Dimensionality Reduction

6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning

7. Combining Different Models for Ensemble Learning

8. Applying Machine Learning to Sentiment Analysis

9. Embedding a Machine Learning Model into a Web Application

10. Predicting Continuous Target Variables with Regression Analysis

11. Working with Unlabeled Data – Clustering Analysis

12. Implementing a Multilayer Artificial Neural Network from Scratch

13. Parallelizing Neural Network Training with TensorFlow

14. Going Deeper – The Mechanics of TensorFlow

15. Classifying Images with Deep Convolutional Neural Networks

16. Modeling Sequential Data Using Recurrent Neural Networks

17. Generative Adversarial Networks for Synthesizing New Data

18. Reinforcement Learning for Decision Making in Complex Environments

19. Other Books You May Enjoy

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20. Index

First steps with TensorFlow

In this section, we will take our first steps in using the low-level TensorFlow API. After installing TensorFlow, we will cover how to create tensors in TensorFlow and different ways of manipulating them, such as changing their shape, data type, and so on.

Installing TensorFlow

Depending on how your system is set up, you can typically just use Python's pip installer and install TensorFlow from PyPI by executing the following from your terminal:

pip install tensorflow

This will install the latest stable version, which is 2.0.0 at the time of writing. In order to ensure that the code presented in this chapter can be executed as expected, it is recommended that you use TensorFlow 2.0.0, which can be installed by specifying the version explicitly:

pip install tensorflow==[desired-version]

In case you want to use GPUs (recommended), you need a compatible NVIDIA graphics card, along with the CUDA Toolkit and the NVIDIA cuDNN library to...

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Authors (2)

Sebastian Raschka

Sebastian Raschka

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.

See other products by Sebastian Raschka

Vahid Mirjalili

Vahid Mirjalili

Vahid Mirjalili is a deep learning researcher focusing on CV applications. Vahid received a Ph.D. degree in both Mechanical Engineering and Computer Science from Michigan State University.

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