1. CRCV REU 2019
Aaron Honculada

2. Theory Deep Learning Artificial Neural Network (ANN) Backpropagation
Gradient Descent
Loss Function
Activation Function
Hyper Parameters
Useful for classification

3. Theory Convolutional Neural Network (CNN) Convolution 3D Convolution
Residual Network
Inception Model
[1] Learning Spatiotemporal Features with 3D Convolutional Networks

4. Theory Recurrent Neural Network (LSTM) Auto-Encoder
Sequential Data
Auto-Encoder
Generative Adversarial Network (GAN)
MAC cell
[1] COMPOSITIONAL ATTENTION NETWORKS FOR MACHINE REASONING

5. Models ResNet I3D [1] Deep Residual Learning for Image Recognition
[2] Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

6. Thanks to Robert, Aisha, and Kartik