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.
Table of Contents (9 chapters)

Scraping Web Page Data

In the spirit of leveraging the internet as a database, we can think about acquiring data from web pages either by scraping content or by interfacing with web APIs. Generally, scraping content means getting the computer to read data that was intended to be displayed in a human-readable format. This is in contradistinction to web APIs, where data is delivered in machine-readable formats – the most common being JSON.

In this topic, we will focus on web scraping. The exact process for doing this will depend on the page and desired content. However, as we will see, it's quite easy to scrape anything we need from an HTML page so long as we have an understanding of the underlying concepts and tools. In this topic, we'll use Wikipedia as an example and scrape tabular content from an article. Then, we'll apply the same techniques to scrape...