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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Deploying a Model as a Web Application

In this section, we will deploy our model as a web application. We will use an example web application—called "cryptonic"—to deploy our model, exploring its architecture so that we can make modifications in the future. The intention is to have you use this application as a starter for more complex applications; a starter that is fully working and can be expanded as you see fit.

Aside from familiarity with Python, this topic assumes familiarity with creating web applications. Specifically, we assume that you have some knowledge about web servers, routing, the HTTP protocol, and caching. You will be able to locally deploy the demonstrated cryptonic application without extensive knowledge of these topics, but learning these topics will make any future development much easier.

Finally, Docker is used to deploy our web...

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