Introduction
CSE 891: Deep Learning
Wednesday September 02, 2020
Book (Optional)
Machine Learning and Neural Networks
Architectures for Pattern Recognition
- Classical architectures for pattern recognition: Speech Recognition
- Classical architectures for pattern recognition: Image Recognition
Deep Learning = Learning Hierarchical Representations
- Deep Architecture: more than one stage of non-linear feature extraction
Trainable Feature Hierarchies: End-to-End Learning
- A hierarchy of trainable feature transforms
- Each module transforms its input representation into a higher-level representation.
- High-level features are more global and more invariant
- Low-level features are shared among categories
- Deep Learning Goal: Make all modules trainable and get them to learn appropriate representations.
Deep Learning
- Deep Learning: many layers (stages) of processing.
- For e.g., this network recognizes objects in images,
- Each box consists of many neuron-like units.
Deep Learning
- You can visualize what a learned feature is responding to by finding an image that excites it. (We’ll see how to do this.)
- Higher layers in the network often learn higher-level, more interpretable representations
