Schedule

Suggested Readings:

• Chapter 1 of the AI textbook by Russell and Norvig

Additional References

• Know your brain
• McCulloch-Pitts Neuron, 1943
• Hebbian Learning Princile by D. Hebb, 1949
• Neocognitron by Fukushima, 1980 and an excellent Video Demo
• 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:

• Chapter-1 of Michael Nielson’s NNDL textbook
• Convergence proof for Perceptron
• MP Neuron

Suggested Readings:

• Chapter-1 of Michael Nielson’s NNDL textbook
• Chapter-4 and 5 of Bishops’ book

Suggested Readings:

• Chapter 4 of Michael Nielson’s NNDL book
• Deep Mind Blog on Universal Approximation Theorem
• Approximation by Superposition of Sigmoid Functions
• Approximation Capabilities of Muitilayer Feedforward Networks

Suggested Readings:

• ML chapter from Goodfellow et al. DL Book
• Numerical Computation chapter from Goodfellow et al DL book

Suggested Reading

• Chapter 2 in Michael Nielson’s NNDL textbook

Additional Readings:

• Paper by Rumelhart et al. that popularized backpropagation

Suggested Reading

• Chapter 2 in Michael Nielson’s NNDL textbook

Additional Readings:

• Paper by Rumelhart et al. that popularized backpropagation

Suggested Readings:

• Chapter-7 from Bishop’s Book
• Ruder’s blog on variants of gradient-based updates
• Chapter on Optimization from Goodfellow et al. book
• Chapter 5 from Michael Nielson’s NNDL book

Suggested Readings:

• Chapter 9 on Regularization from Bishop’s book
• Chapter on Regularization from Goodfellow et al. book
• Chapter 5 from Michael Nielson’s NNDL book

Suggested Readings:

• CNNs module from CS231n
• Chapter 10 from Bishop’s book
• CNNs Chapter from Goodfellow et al. DL book

Suggested Readings:

• CNNs module from CS231n
• CNNs Chapter from Goodfellow et al. DL book
• CNNs Chapter from Bishop’s book

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:

• Chapter 10 from Goodfellow et al DL book
• Denny’s tutorial on RNNs
• Colah’s blog on LSTMs
• GRU by Cho et al. 2014

Suggested Readings:

• Notes by Elena Voita
• GloVe Paper
• Word2Vec Paper