Lecture 1 - Overview of Machine Learning and Deep Learning
Lecture 2 - Single Layer Perceptron
Lecture 3 - Hands-on on Single Layer Perceptron
Lecture 4 - Multi-Layer Perceptron
Lecture 5 - Activation and Loss Functions
Lecture 6 - Gradient descent algorithm
Lecture 7 - Backpropagation in Neural Network
Lecture 8 - Varients of Gradient descent and Momentum based techniques
Lecture 9 - Regularization techniques in Neural Networks
Lecture 10 - Hands-on on MLP for classification problem
Lecture 11 - Fundamentals of Image representation and Image preprocessing and Data augmentation
Lecture 12 - Introduction to Convolutional Neural Networks, Inspiration behind CNN, Key Components of CNN
Lecture 13 - Convolution for RGB, Pooling Layer and Flatten Layer
Lecture 14 - Batch Normalization
Lecture 15 - Hands-on session on building simple CNN model
Lecture 16 - Architecture and Implementation of AlexNet
Lecture 17 - VGGNet (VGG16 and VGG19) and GoogleNet
Lecture 18 - Introduction to ResNet (ResNet 34 and ResNet50)
Lecture 19 - Introduction to Transfer Learning
Lecture 20 - Hands-on on standard CNN architectures and transfer learning
Lecture 21 - Hands-on on building and deep CNN ensemble model
Lecture 22 - Need for XAI, PCI, Occlusion method
Lecture 23 - SHAP and LIME
Lecture 24 - Guided backpropagation
Lecture 25 - Grad-CAM and Class Activation Maps (CAMs)
Lecture 26 - Hands-on implemenation of XAI methods
Lecture 27 - Image segmentation
Lecture 28 - U-Net with Attention and Evaluation Metric for Segmentation
Lecture 29 - Object detection
Lecture 30 - YOLO v1 for object detection
Lecture 31 - Hands-on implemenation of Unet
Lecture 32 - Creating yaml file for the custom dataset
Lecture 33 - Hands-on implemenation of YOLO
Lecture 34 - Introduction to Recurrent Neural Networks
Lecture 35 - Numerical Example for RNN
Lecture 36 - Vanishing and exploding gradients in NN
Lecture 37 - Backpropagation through time (BTT)
Lecture 38 - Hands-on implementation of RNN model and its variants
Lecture 39 - Introduction to LSTM
Lecture 40 - Gated Recurrent Unit
Lecture 41 - LSTM-Encoder Decoder Structure
Lecture 42 - LSTM with attention
Lecture 43 - Hands-on implementation of LSTM
Lecture 44 - Introduction to NLP
Lecture 45 - Text Preprocessing and Text Representations
Lecture 46 - Discrete and Distributed Word Representation
Lecture 47 - Word Embeddings in NLP
Lecture 48 - Hands-on implementation of RNN in NLP application
Lecture 49 - Introduction to Autoencoders
Lecture 50 - Types of Autoencoders
Lecture 51 - VAE Architecture - Encoder part
Lecture 52 - VAE Architecture - Decoder part
Lecture 53 - Reparameterization Trick
Lecture 54 - Hands-on implemenation of Autoencoders
Lecture 55 - Foundations of Language Modeling and Transformers
Lecture 56 - Transformer Architectures and Attention Mechanisms
Lecture 57 - In-context learning and Self-Supervised Learning in LLMs
Lecture 58 - Large Language Models: Tokenization, Generation, and Sampling
Lecture 59 - Pre-training and Instruction Tuning of Large Language Models
Lecture 60 - Reinforcement Learning for Aligning Large Language Models
Lecture 61 - Reasoning, Retrieval, and Efficiency in Post-trained LLMs
Lecture 62 - Evaluation, Benchmarking, and Impact of Foundation Models
Lecture 63 - LLMs demo - TA
Lecture 64 - Diffusion Models for Generative Modelling
Lecture 65 - GANs and Diffusion models demo - TA
