Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms

With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field.

Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. If you’re familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started.

• Examine the foundations of machine learning and neural networks
• Learn how to train feed-forward neural networks
• Use TensorFlow to implement your first neural network
• Manage problems that arise as you begin to make networks deeper
• Build neural networks that analyze complex images
• Perform effective dimensionality reduction using autoencoders
• Dive deep into sequence analysis to examine language
• Understand the fundamentals of reinforcement learning

• Deep Learning: A Practitioner's Approach
• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
• Learning TensorFlow: A Guide to Building Deep Learning Systems
• Deep Learning with Python
• Introduction to Machine Learning with Python: A Guide for Data Scientists
• TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning
• Practical Statistics for Data Scientists: 50 Essential Concepts
• Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition
• Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
• Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit