[Mar-Apr ‘19] Visual Recognition (Grad Level)
Basic Info
- Where: IIIT-Bangalore
- When: March/April 2019
- Who: Anush Sankaran, IBM Research AI (co-instructed with Prof. Dinesh Babu Jayagopi)
Course Overview
Lecture | Topic | Content | Slides | Notes |
---|---|---|---|---|
1 | Introduction to Image Classification, Neural Networks, and Optimization | - What is visual recognition? - Logistic regression - Stochastic Gradient Descent - Multilayer perceptron - Backpropagation - DL + ML Pipeleine | slides | |
2 | Unsupervised Feature Learning, Autoencoders, Convolutional Neural Networks | - Popular applications of DL - Stacked autoencoders - Convolution & Pooling layers - Convolutional autoencoder | slides | Notebook |
3 | Hyper-parameter optimization, Training Process | Convolutional neural network - One time model setup - Hyper-parameter optimization | slides | Notebook |
4 | Different CNN Architectures | Data Augmentation - Transfer Learning - Comparison of Different CNN Architectures - Watson Studio Hands-on | slides | Watson Studio: How To |
5 | Generative Modelling | Unsupervised learning - Distribution fitting - PixelRNN/CNN - Variational Autoencoder (VAE) - Generative Adversarial Network (GAN) - Open source GAN toolkit | slides | Open source GAN Toolkit |
6 | CNN Visualization and Face Recognition | Neuron Visualization - Guided BackProp - Grad-CAM - Face Classification - Face Generation - DeepFake - Model Trust | slides |
Acknowledgement
References and they have better slides! With huge respects to their slides, hard work, and efforts, I acknowledge them and only makes sense to reuse some part of their slides!
- Book on “Deep Learning” (https://www.deeplearningbook.org/ )
- CS231n: Convolutional Neural Networks for Visual Recognition (http://vision.stanford.edu/teaching/cs231n/index.html )
- CS 6501-004: Deep Learning for Visual Recognition (http://vicenteordonez.com/deeplearning/ )
- ECE 6504 Deep Learning for Perception (https://computing.ece.vt.edu/~f15ece6504/ )
[Oct-Dec ‘19] Foundations of Machine Learning (MBA Students)
Basic Info
- Where: ISME-Bangalore
- When: October/ November 2019
- Who: Anush Sankaran, IBM Research AI
Course Overview
Lecture | Topic | Slides | Notes |
---|---|---|---|
1 | Intro to ML, Discovering ML Use Cases & ML in Business | slides | |
2 | Python- Hands On, Supervised Learning & Regression | slides | Code Ex 1 Code Ex 2 |
3 | Neural Network - 1, Neural Network -2 & Hands ON | slides | Code Ex 3 Code Ex 4 |
4 | Kernel Learning & SVM, Practical Advice for ML projects. | slides | |
5 | Boosting, Decision Trees, Random Forest, & xgBoost | slides | |
6 | Unsupervised Learning, Clustering & Dimensionality Reduction | slides | |
7 | Time Series Data Analysis, Imputation & Prediction Systems | slides | |
8 | ML Use Cases from Products & Research | slides |
Acknowledgement
Multiple references are borrowed from different sources of internet and different other courses, and they have better slides! With huge respects to their slides, hard work, and efforts, I acknowledge them and only makes sense to reuse some part of their slides!