Book description
- Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe
- Gain the fundamentals of deep learning with mathematical prerequisites
- Discover the practical considerations of large scale experiments
- Take deep learning models to production
Table of contents
- Cover
- Frontmatter
- 1. Introduction to Deep Learning
- 2. Machine Learning Fundamentals
- 3. Feed Forward Neural Networks
- 4. Introduction to Theano
- 5. Convolutional Neural Networks
- 6. Recurrent Neural Networks
- 7. Introduction to Keras
- 8. Stochastic Gradient Descent
- 9. Automatic Differentiation
- 10. Introduction to GPUs
- 11. Introduction to Tensorflow
- 12. Introduction to PyTorch
- 13. Regularization Techniques
- 14. Training Deep Learning Models
- Backmatter
Product information
- Title: Deep Learning with Python: A Hands-on Introduction
- Author(s):
- Release date: April 2017
- Publisher(s): Apress
- ISBN: 9781484227664
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