Chapter 4. Neural Networks and Deep Learning
In this chapter, we will cover one of the most exciting fields in artificial intelligence and machine learning: Deep Learning. This chapter will walk through the most important concepts necessary to apply deep learning effectively. The topics that we will cover in this chapter are as follows:
- Essential neural network theory
- Running neural networks on the GPU or CPU
- Parameter tuning for neural networks
- Large scale deep learning on H2O
- Deep learning with autoencoders (pretraining)
Deep learning emerged from the subfield of artificial intelligence that developed neural networks. Strictly speaking, any large neural network can be considered deep-learning. However, recent developments in deep architectures require more than setting up large neural networks. The difference between deep architectures and normal multilayer networks is that a deep architecture consists of multiple preprocessing and unsupervised steps that detect latent dimension in...
