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)

Preface

This Learning Path takes a step-by-step approach to teach you how to get started with data science, machine learning, and deep learning. Each module is designed to build on the learning of the previous chapter. The book contains multiple demos that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context.

In the first part of this Learning Path, you will learn entry-level data science. You'll learn about commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world.

In the second part, you'll be introduced to neural networks and deep learning. You will then learn how to train, evaluate, and deploy Tensorflow and Keras models as real-world web applications. By the time you are done reading, you will have the knowledge to build applications in the deep learning environment and create elaborate data visualizations and predictions.