Java Deep Learning Essentials > PACKT 원서리스트

1: Deep Learning Overview
Transition of AI
Things dividing a machine and human
AI and deep learning
Summary

2: Algorithms for Machine Learning – Preparing for Deep Learning
Getting started
The need for training in machine learning
Supervised and unsupervised learning
Machine learning application flow
Theories and algorithms of neural networks
Summary

3: Deep Belief Nets and Stacked Denoising Autoencoders
Neural networks fall
Neural networks' revenge
Deep learning algorithms
Summary

4: Dropout and Convolutional Neural Networks
Deep learning algorithms without pre-training
Dropout
Convolutional neural networks
Summary

5: Exploring Java Deep Learning Libraries – DL4J, ND4J, and More
Implementing from scratch versus a library/framework
Introducing DL4J and ND4J
Implementations with ND4J
Implementations with DL4J
Summary

6: Approaches to Practical Applications – Recurrent Neural Networks and More
Fields where deep learning is active
The difficulties of deep learning
The approaches to maximizing deep learning possibilities and abilities
Summary

7: Other Important Deep Learning Libraries
Theano
TensorFlow
Caffe
Summary

8: What's Next?
Breaking news about deep learning
Expected next actions
Useful news sources for deep learning
Summary

Appendix A: Index