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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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