Deep Learning Cookbook: Practical Recipes to Get Started Quickly

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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


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