BME 69500DL

(cross-listed as ECE 695)

Deep Learning

by

Avi Kak   and   Charles Bouman


Spring 2020

PREREQUISITES FOR THIS CLASS

This class is substantially self-contained. All you need in order to enroll for this class is that you be a graduate student in engineering, computer science, quantitative psychology, mathematics, etc., and that you possess at least a rudimentary knowledge of programming in Python.



Online Lectures: Video Lectures

Week 1   Tuesday,  Jan 14: Course Intro: (slides)

  Thursday, Jan 16: (Bouman) (slides) Intro to ML: What is machine learning?; single layer
  neural networks; the MSE loss function


  (Link to Piazza class web cite. Can be used for asking and answering questions.)
Week 2   Tuesday,  Jan 21: (Kak) (slides) Python OO for DL

  Thursday, Jan 23: (Bouman) (slides) Intro to ML: Gradient descent optimization;
Week 3   Tuesday,  Jan 28: (Kak) (slides) Image and text datasets for DL research, Torchvision, Torchtext

  Thursday, Jan 30: (Bouman) (slides) Intro to ML: Tensors; GD for single layer NNs


  (Some of the Torchvision related material during this week will be illustrated with the functionality built into the
  RegionProposalGenerator module that you can access by clicking here.)
Week 4   Tuesday,  Feb 4:  (Kak) (slides) Autograd for automatic differentiation and computational graphs

  Thursday, Feb 6:  (Bouman) (slides) Intro to ML: Optimization of deep functions; GD on acyclic
  graph structures; general loss functions


  (Several of the key ideas used for automatic differentiation in Autograd will be explained with the help of the
  ComputationalGraphPrimer module that you can access by clicking here.)
Week 5   Tuesday,   Feb 11: (Kak) (slides) A first introduction to torch.nn for designing CNNs

  Thursday, Feb 13:: (Bouman) (slides) Intro to NNs: Convolutional NNs; adjoint gradient for CNNs


  (We will talk about torch.nn with the help of the new DLStudio module that you can access by clicking here.)
Week 6   Tuesday,  Feb 18: (Kak) (slides) Demystifying the Convolutions in PyTorch

  Thursday, Feb 20: (Bouman) (slides) Intro to ML: Probability and estimation; Frequentist
  and Bayesian estimation; the bias variance tradeoff
Week 7   Tuesday,  Feb 25: (Kak) (slides) Using Skip Connections to Mitigate the Problem of Vanishing
  Gradients in Deep Networks.

  Thursday, Feb 27: Mid-Term Test 1 ( exam , exam solution )


  (The material related to skip connections will be explained with the help an in-class demo based on the inner class SkipConnections
  of version 1.0.6 of the DLStudio module that you can access by clicking here.)
Week 8   Tuesday,  March 3: (Kak) (slides) Object Detection and Localization with Deep Networks

  Thursday, March 5: (Bouman) (slides) Intro to Optimization: stochastic gradient descent;
  momenturn; ADAM optimization

  (The material related to object detection and localization will be explained with the help an in-class demo based on the inner class
  DetectAndLocalize of version 1.0.7 of the DLStudio module that you can access by clicking here.)
Week 9   Tuesday,  March 10:   (Kak) (slides) Graph Based Algorithms for Generating Region Proposals for
  Object Detection

  Thursday, March 12:   (Bouman) (slides) Training Techniques: vanishing gradient; ResNet; U-Net;
  Transfer Learning; Data Augmentation;


  (Forming region proposals is critical to object detection. While the more recent frameworks use CNNs for generating
   region proposals, using the older approach based on the more traditional algorithms is still important for many
   problem domains. The traditional approach will be illustrated with the RegionProposalGenerator module that you can
   access by clicking here.)
Week 10  
Spring Break

Week 11   Tuesday,  March 24: (Kak) (slides) Semantic Segmentation of Images with Fully Convolutional Networks

  Thursday, March 26: (Bouman) Intro to Optimization: non-linear back propagation and the
                                    chain rule; forward/backward propagation; automated differentiation.


  (The material related to semantic segmentation is based on the nUnet network which is my implementation of the Unet. You will find the
  code for mUnet in version 1.0.9 of the DLStudio module that you can access by clicking here.)
Week 12   Tuesday,  March 31: (Kak) Recurrent Networks for language modeling and seq2seq learning

  Thursday, April 2: (Bouman) Advanced Optimization: convex versus non-convex
                                    optimization; momentum and Adam optimizer
Week 13   Tuesday,  April 7: (Kak) Word embeddings for textural classification, torch.nn.Embedding

  Thursday, April 9:     (Bouman) Advanced Optimization: batch normalization; regularization
                                    methods; transfer learning.
Week 14   Tuesday,  April 14:     (Kak) GANs for domain adaptation and domain repair

  Thursday, April 16:     (Bouman) Advanced Optimization: minimax optimization; saddle points
                                    and local minimum; hyperparameters optimization.
Week 15   Tuesday,  April 21: (Bouman) The theory underlying reinforcement learning

  Thursday, April 23: Tuesday,  April 28: Mid-Term Test 2
Week 16   (Kak) Reinforcement learning with the Double Q algorithm and the
                                    DQN network

  Thursday, April 30: (Bouman) Variational autoencoders; variational and conditional GANs.


Recommended Books:

- https://www.manning.com/books/grokking-deep-learning
- Deep Learning
- Neural Networks and Deep Learning
- Data Science from Scratch
- Python Machine Learning
- http://machinelearningmastery.com/deep-learning-courses/
- Pytorch tutorials

Links to documentation pages you will frequently be visiting:

- Master documentation page for PyTorch
- A direct link to the torch.nn module
- Master documentation page for Torchvision
- A direct link to Torchvision Transforms
- Master documentation page for Torchtext
- A useful summary of many of the most basic operations on PyTorch Tensors
- The homepage for CIFAR-10 and CIFAR-100 image datasets

Recommended Supplementary Course Material:

- CMU deep learning
- Stanford Class by FeiFei and Karpathy. For videos
- Udacity Deep Learning nano-degree

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