| Date | Topic | Content | Slides | Notes |
|---|---|---|---|---|
| 22nd March, 2019 (Friday) | Introduction to Image Classification, Neural Networks, and Optimization | - What is visual recognition? - Logistic regression - Stochastic Gradient Descent - Multilayer perceptron - Backpropagation - DL + ML Pipeleine | slides | |
| 30th March, 2019 (Saturday) | Unsupervised Feature Learning, Autoencoders, Convolutional Neural Networks | - Popular applications of DL - Stacked autoencoders - Convolution & Pooling layers - Convolutional autoencoder | slides | Notebook |
| 6th April, 2019 (Saturday) | Hyper-parameter optimization, Training Process | Convolutional neural network - One time model setup - Hyper-parameter optimization | slides | Notebook |
| 10th April, 2019 (Wednesday) | Different CNN Architectures | Data Augmentation - Transfer Learning - Comparison of Different CNN Architectures - Watson Studio Hands-on | slides | Watson Studio: How To |
| 20th April, 2019 (Saturday) | Generative Modelling | Unsupervised learning - Distribution fitting - PixelRNN/CNN - Variational Autoencoder (VAE) - Generative Adversarial Network (GAN) - Open source GAN toolkit | slides | Open source GAN Toolkit |
| 27th April, 2019 (Saturday) | CNN Visualization and Face Recognition | Neuron Visualization - Guided BackProp - Grad-CAM - Face Classification - Face Generation - DeepFake - Model Trust | slides |
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!