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)

Web Scraping and Interactive Visualizations

So far in this book, we have focused on using Jupyter to build reproducible data analysis pipelines and predictive models. We'll continue to explore these topics in this chapter, but the main focus here is data acquisition. In particular, we will show you how data can be acquired from the web using HTTP requests. This will involve scraping web pages by requesting and parsing HTML. We will then wrap up this chapter by using interactive visualization techniques to explore the data we've collected.

The amount of data available online is huge and relatively easy to acquire. It's also continuously growing and becoming increasingly important. Part of this continual growth is the result of an ongoing global shift from newspapers, magazines, and TV to online content. With customized news feeds available all the time on cell phones...