Deep Learning Cookbook: Practical Recipes to Get Started Quickly

Deep Learning Cookbook: Practical Recipes to Get Started Quickly

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