Deep Learning in Practice

Deep Learning is a subfield of machine learning inspired by the structure and function of artificial neural networks. This practical guidebook gives you an overview of the key technologies and explains the basic functionality involved in deep learning.

Ramon Wartala uses Python-based sample applications to demonstrate how to use the Caffe/Caffe2 and TensorFlow frameworks, and his real-world examples are sure to get your coding juices flowing. Wartala also talks about why graphics cards, big data, and cloud computing are so important to deep learning. If you are already familiar with Python, NumPy, and matplotlib, this book will help you gain your first practical experience with deep learning applications.

Background

• The methods deep learning is based on
• Applications such as machine translation, speech recognition, and image recognition at Google, Facebook, IBM, and Amazon

The Docker Toolbox

• The Docker container provided with the book provides all the tools and programs you need to duplicate the examples included in the text and design your own deep learning applications
• Get to know the environment: Jupyter Notebook, sample data sets, web scraping

Getting Started

• Introduction to Caffe/Caffe2 and TensorFlow
• Programming your own deep learning applications: Handwriting recognition, image recognition and classification, deep dreaming
• Solutions for big data scenarios: Distributed applications, Spark, cloud systems
• Implementing models in productive systems

The sample code and Docker container are available for download at:

https://github.com/rawar/deeplearning

https://hub.docker.com/r/rawar/deeplearning/