Schedule

Suggested Readings:

• McCulloch-Pitts Neuron, 1943
• Hebbian Learning Princile by D. Hebb, 1949
• Neocognitron by Fukushima, 1980
• Backprop for training NNs by Rumelhart et al. 1986
• Backprop applied to MNIST by Lecun, 1989
• AlexNet by Krizhevsky et al. 2012
• ResNet by He et al. 2015

Suggested Readings:

• Convergence proof for Perceptron
• MP Neuron

Suggested Readings:

Suggested Readings:

• Approximation by Superposition of Sigmoid Functions
• Approximation Capabilities of Muitilayer Feedforward Networks
• Chapter 4 of Michael Nielson’s book

Suggested Readings:

Suggested Readings:

• Paper by Rumelhart et al. that popularized backpropagation

Suggested Readings:

• Chap-2 from Michael Nielson’s online book
• Forward Forward Algorithm by Prof. Hinton

Suggested Readings:

• Chap-3 from Goodfellow et al. 2016 book

Suggested Readings:

Suggested Readings:

Suggested Readings:

• Chap-7 on Regularization from I Goodfellow et al. 2016
• Chap-8 on Optimization for DNNs from I Goodfellow et al. 2016

Suggested Readings:

• Chap-7 on Regularization from I Goodfellow et al. 2016
• Chap-8 on Optimization for DNNs from I Goodfellow et al. 2016

Suggested Readings:

• Rich Feature Hierarchies by Girshick et al. CVPR 2014
• Visualizing and Understanding CNNs by Zeiler and Fergus ECCV 2014
• Deep Inside CNNs by Simonyan et al. ICLRW 2014
• Class Activation Maps (CAM) by B Zhou et al. CVPR 2016
• Grad-CAM by Selvaraju et al. NIPSW 2016, ICCV 2017
• CNN-Fixations by Mopuri et al. TIP 2018
• Texture Synthesis using CNNs by Gatys et al. NeurIPS 2015
• DeepDream
• Neural style transfer by Gatys et al. CVPR 2016
• Understanding Deep Representations by Inverting them by A Mahendran et al. CVPR 2015
• Inverting Visual Representations with CNNs by A Dosovitskiy et al. CVPR 2016-

Suggested Readings:

• Denny’s tutorial on RNNs
• Colah’s blog on LSTMs
• GRU by Cho et al. 2014

Suggested Readings:

Suggested Readings:

• Sequence to sequence learning by Sutskever et al. NeurIPS 2014
• Neural Machine Translation with aligning by Bahdanau et al. ICLR 2015
• Show Attend and Tell by Xu et al. 2015

Suggested Readings:

• The Illustrated Transformer
• Attention is all we need!
• Computational complexity discussion
• My slides on self-attention layer for CNNs

Suggested Readings:

• IJCAI-ECAI 2018 Tutorial on Generative Modeling

Suggested Readings:

Suggested Readings:

Suggested Readings:

• Dive into DL, chapter on GANs
• DC GAN by Radford et al. NeurIPS 2016