Feature Engineering for Machine Learning Models: Principles and Techniques for Data Scientists | Learning TensorFlow: A Guide to Building Deep Learning Systems | Deep Learning (Adaptive Computation and Machine Learning series) | Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems | Deep Learning | Introduction to Machine Learning With Python | Statistics for Data Scientists
With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one thatâs 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 youâre 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
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