ThinkML

My thoughts on Machine Learning and Related.

About Me!Anush Sankaran

[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!