Meta Learning (BITS G513)

Meta Learning Course Material (Spring 2021, at BITS-Goa)

Week 1

Tutorial

• Slides
• Summary

Lab

• 1A: Basic Gradient Computation. Leaf and Non-Leaf Variables

• 1B: Simple Classification and Regression

Week 2

Tutorial

• Slides
• Summary

Lab

• 2A: Deep Learning - Gradient Descent and Backpropagation

• 2B: Deep Learning - With Pytorch

Week 3

Tutorial

• Slides
• Summary

Lab

• 3A: Introduction to Convolutions and 1D Convolutions

• 3B: CNNs and 2D Convolutions

Week 4

Tutorial

• Slides
• Summary

Lab

• 4: Convolution on Real Audio, 1D CNN based Emotion Classification

Week 5

Tutorial

• Summary

Lab

• 5: Transfer Learning and FineTuning Pre-Trained models

Week 6

Lab

• 6A: SGD + Momentum and BatchNorm experiments
• 6B: AdaGrad, RMSprop, Adam, Optimizer Benchmarking

Week 7

Lab

• 7: Sequence Classification using RNNs and LSTMs

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Staff

Instructors

• Gautam Shroff
• Tirtharaj Dash

Teaching Assistants

• Sharad Chitlangia
• Aditya Ahuja

The initial part of the coursework (on Deep Learning) has been adapted from Yann LeCun and Alfredo Canziani's course on Deep Learning at NYU

Google group

A non-public group to communicate some course specific messages is created: Join/View