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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Summary

In this chapter, we scraped web page tables and then used interactive visualizations to study the data.

We started by looking at how HTTP requests work, focusing on GET requests and their response status codes. Then, we went into the Jupyter Notebook and made HTTP requests with Python using the Requests library. We saw how Jupyter can be used to render HTML in the notebook, along with actual web pages that can be interacted with. After making requests, we saw how Beautiful Soup can be used to parse text from the HTML, and used this library to scrape tabular data.

After scraping two tables of data, we stored them in pandas DataFrames. The first table contained the central bank interest rates for each country and the second table contained the populations. We combined these into a single table that was then used to create interactive visualizations.

Finally, we used Bokeh...

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