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Applied Deep Learning with Python

You're reading from Applied Deep Learning with Python Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions

Product type Paperback

Published in Aug 2018

Publisher

ISBN-13 9781789804744

Length 334 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Authors (2):

Galea

Luis Capelo

View More author details

Table of Contents (14) Chapters

Title Page

Copyright and Credits

Packt Upsell

Contributors

Preface

1. Jupyter Fundamentals FREE CHAPTER

2. Data Cleaning and Advanced Machine Learning

3. Web Scraping and Interactive Visualizations

4. Introduction to Neural Networks and Deep Learning

5. Model Architecture

6. Model Evaluation and Optimization

7. Productization

1. Other Books You May Enjoy

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Index

Configuring a Deep Learning Environment

Before we finish this chapter, we want you to interact with a real neural network. We will start by covering the main software components used throughout this book and make sure that they are properly installed. We will then explore a pretrained neural network and explore a few of the components and operations discussed earlier in the What are Neural Networks? section.

Software Components for Deep Learning

We'll use the following software components for deep learning:

Python 3

We will be using Python 3 . Python is a general-purpose programming language which is very popular with the scientific community—hence its adoption in deep learning. Python 2 is not supported in this book but can be used to train neural networks instead of Python 3. Even if you chose to implement your solutions in Python 2, consider moving to Python 3 as its modern feature set is far more robust than that of its predecessor.

TensorFlow

TensorFlow is a library used for performing mathematical...

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Authors (2)

Galea

Galea

Alex Galea has been professionally practicing data analytics since graduating with a masters degree in physics from the University of Guelph, Canada. He developed a keen interest in Python while researching quantum gases as part of his graduate studies. Alex is currently doing web data analytics, where Python continues to play a key role in his work. He is a frequent blogger about data-centric projects that involve Python and Jupyter Notebooks.

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Luis Capelo

Luis Capelo

Luis Capelo is a Harvard-trained analyst and a programmer, who specializes in designing and developing data science products. He is based in New York City, America. Luis is the head of the Data Products team at Forbes, where they investigate new techniques for optimizing article performance and create clever bots that help them distribute their content. He worked for the United Nations as part of the Humanitarian Data Exchange team (founders of the Center for Humanitarian Data). Later on, he led a team of scientists at the Flowminder Foundation, developing models for assisting the humanitarian community. Luis is a native of Havana, Cuba, and the founder and owner of a small consultancy firm dedicated to supporting the nascent Cuban private sector.

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