Deep Learning Cookbook: Practical Recipes to Get Started Quickly

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

• Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning
• Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data
• Deep Learning with Python
• Deep Learning: A Practitioner's Approach
• Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
• Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play
• The Hundred-Page Machine Learning Book
• Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning
• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
• Neural Networks and Deep Learning: A Textbook