Deep Learning Cookbook

Douwe Osinga

ISBN 10: 149199584X / ISBN 13: 9781491995846

Published by O'Reilly Media, US, 2018

About this Item

Deep learning doesn't have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, you'll learn how to solve deep-learning problems for classifying and generating text, images, and music. Each chapter consists of several recipes needed to complete a single project, such as training a music recommending system. Author Douwe Osinga also provides a chapter with half a dozen techniques to help you if you're stuck. Examples are written in Python with code available on GitHub as a set of Python notebooks. You'll learn how to: Create applications that will serve real users Use word embeddings to calculate text similarity Build a movie recommender system based on Wikipedia links Learn how AIs see the world by visualizing their internal state Build a model to suggest emojis for pieces of text Reuse pretrained networks to build an inverse image search service Compare how GANs, autoencoders and LSTMs generate icons Detect music styles and index song collections.

Seller Inventory # LU-9781491995846

Bibliographic Details

• Title: Deep Learning Cookbook
• Publisher: O'Reilly Media, US
• Publication Date: 2018
• Language: English
• ISBN 10: 149199584X
• ISBN 13: 9781491995846
• Binding: Paperback
• Condition: New
• Weight: 454 grams

About this title

Synopsis

Deep learning doesn’t have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, you’ll learn how to solve deep-learning problems for classifying and generating text, images, and music.

Each chapter consists of several recipes needed to complete a single project, such as training a music recommending system. Author Douwe Osinga also provides a chapter with half a dozen techniques to help you if you’re stuck. Examples are written in Python with code available on GitHub as a set of Python notebooks.

You’ll learn how to:

• Create applications that will serve real users
• Use word embeddings to calculate text similarity
• Build a movie recommender system based on Wikipedia links
• Learn how AIs see the world by visualizing their internal state
• Build a model to suggest emojis for pieces of text
• Reuse pretrained networks to build an inverse image search service
• Compare how GANs, autoencoders and LSTMs generate icons
• Detect music styles and index song collections

About the Author

Douwe Osinga is an experienced Software Engineer, formerly with Google, and founder of three startups. He maintains a popular software project website, partly focused on machine learning (https://douweosinga.com/projects/machine_learning).