Learn how to solve challenging machine learning problems with TensorFlow, Google’s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects in images, understanding text, analyzing video, and predicting the properties of potential medicines.
TensorFlow for Deep Learning teaches concepts through practical examples and helps you build knowledge of deep learning foundations from the ground up. It’s ideal for practicing developers with experience designing software systems, and useful for scientists and other professionals familiar with scripting but not necessarily with designing learning algorithms.
- Learn TensorFlow fundamentals, including how to perform basic computation
- Build simple learning systems to understand their mathematical foundations
- Dive into fully connected deep networks used in thousands of applications
- Turn prototypes into high-quality models with hyperparameter optimization
- Process images with convolutional neural networks
- Handle natural language datasets with recurrent neural networks
- Use reinforcement learning to solve games such as tic-tac-toe
- Train deep networks with hardware including GPUs and tensor processing units
Table of Contents
Preface
1. Introduction to Deep Learning
2. Introduction to TensorFlow Primitives
3. Linear and Logistic Regression with TensorFlow
4. Fully Connected Deep Networks
5. Hyperparameter Optimization
6. Convolutional Neural Networks
7. Recurrent Neural Networks
8. Reinforcement Learning
9. Training Large Deep Networks
10. The Future of Deep Learning
Index
