Deep Learning Cookbook （True PDF/2018）

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English | 2018 | ISBN: 1491995846 | 252 Pages | True PDF| 13 MB

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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