Book Image

Applied Deep Learning with Python

By : Alex Galea, Luis Capelo
Book Image

Applied Deep Learning with Python

By: Alex Galea, Luis Capelo

Overview of this book

Taking an approach that uses the latest developments in the Python ecosystem, you’ll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before you train your first predictive model. You’ll then explore a variety of approaches to classification such as support vector networks, random decision forests and k-nearest neighbors to build on your knowledge before moving on to advanced topics. After covering classification, you’ll go on to discover ethical web scraping and interactive visualizations, which will help you professionally gather and present your analysis. Next, you’ll start building your keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data. You’ll then be guided through a trained neural network, which will help you explore common deep learning network architectures (convolutional, recurrent, and generative adversarial networks) and deep reinforcement learning. Later, you’ll delve into model optimization and evaluation. You’ll do all this while working on a production-ready web application that combines TensorFlow and Keras to produce meaningful user-friendly results. By the end of this book, you’ll be equipped with the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively.

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Table of Contents (9 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Jupyter Fundamentals
Jupyter Fundamentals
Basic Functionality and Features
Our First Analysis - The Boston Housing Dataset
Summary
2
Data Cleaning and Advanced Machine Learning
Data Cleaning and Advanced Machine Learning
Preparing to Train a Predictive Model
Training Classification Models
Summary
3
Web Scraping and Interactive Visualizations
Web Scraping and Interactive Visualizations
Scraping Web Page Data
Interactive Visualizations
Summary
4
Introduction to Neural Networks and Deep Learning
Introduction to Neural Networks and Deep Learning
What are Neural Networks?
Configuring a Deep Learning Environment
Summary
5
Model Architecture
Model Architecture
Choosing the Right Model Architecture
Using Keras as a TensorFlow Interface
Summary
6
Model Evaluation and Optimization
Model Evaluation and Optimization
Model Evaluation
Hyperparameter Optimization
Summary
7
Productization
Productization
Handling New Data
Deploying a Model as a Web Application
Summary
8
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Using Keras as a TensorFlow Interface

This section focuses on Keras. We are using Keras because it simplifies the TensorFlow interface into general abstractions. In the backend, the computations are still performed in TensorFlow—and the graph is still built using TensorFlow components—but the interface is much simpler. We spend less time worrying about individual components, such as variables and operations, and spend more time building the network as a computational unit. Keras makes it easy to experiment with different architectures and hyperparameters, moving more quickly towards a performant solution.

As of TensorFlow 1.4.0 (November 2017), Keras is now officially distributed with TensorFlow as tf.keras. This suggests that Keras is now tightly integrated with TensorFlow and that it will likely continue to be developed as an open source tool for a long period...

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