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.

Related Content you might be interested in

Current Title:

Small book image
Applied Deep Learning with Python
Alex Galea | Luis Capelo
Aug, 2018 | 334 pages
Small book image
Mastering Predictive Analytics with scikit-learn and TensorFlow
Alvaro Fuentes
Sep, 2018 | 154 pages
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
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

What are Neural Networks?

Neural networks—also known as Artificial Neural Networks—were first proposed in the 40s by MIT professors Warren McCullough and Walter Pitts.

For more information refer, Explained: Neural networks. MIT News Office, April 14, 2017. Available at:
http://news.mit.edu/2017/explained-neural-networksdeep-learning-0414.

Inspired by advancements in neuroscience, they proposed to create a computer system that reproduced how the brain works (human or otherwise). At its core was the idea of a computer system that worked as an interconnected network. That is, a system that has many simple components. These components both interpret data and influence each other on how to interpret data. This same core idea remains today.

Deep learning is largely considered the contemporary study of neural networks. Think of it as a current name given to neural networks...

Previous Section
End of Section 2
Next Section