IIT Madras DL GenAI Lectures: Introduction to Deep Learning and Generative AI
BSDA2001117 lectures12 weeks
Weeks
- Week 121 lecturesIntroduction to Artificial Neural Networks · Introduction to Pytorch and Tensors · Pytorch Installation and Environment Setup · Tensor - Creation Methods · Tensor - Manipulation Methods · Tensor - Reshaping and Indexing Methods · Pytorch and Numpy Integration · Reproducibility in Pytorch · Hardware Acceleration in Pytorch · Common Pytorch Errors · Linear Regression in Pytorch: Data Creation and Loading · Linear Regression in Pytorch: Data Visualization · Linear Regression in Pytorch: Model Definition · Linear Regression in Pytorch: Custom Loss Function · Linear Regression in Pytorch: Training Loop · Model Serialization in Pytorch · Neuron: Biology to Mathematics · Fundamental Tricks in Deep Learning · Feedforward Network Architecture · Feedforward Network Architecture: Hands-on Example · Activation Functions
- Week 222 lecturesUniversal Approximation Theorem · Forward Pass and Loss Functions · Gradient Descent · Customer Churn Prediction: Overview · Customer Churn Prediction: Data Processing · Customer Churn Prediction: Neural Network Package Imports · Customer Churn Prediction: Neural Network Implementation · Customer Churn Prediction: Weight Initialization · Customer Churn Prediction: Training and Validation Setup · Customer Churn Prediction: Training Loop Implementation · Customer Churn Prediction: Model Training · Backpropagation: Simple Scalar Chain · Backpropagation: Multilayer Perceptron · The XOR Problem: Introduction · The XOR Problem: Forward Pass · The XOR Problem: Backpropagation · Fashion MNIST Classification: Overview and Data Processing · Fashion MNIST Classification: MLP Implementation in Pytorch · Fashion MNIST Classification: Forward Pass · Fashion MNIST Classification: Training Loop · Fashion MNIST Classification: Validation Loop · Fashion MNIST Classification: Model Training
- Week 313 lecturesIntroduction to Vision Modelling · From MLP to Convolution · Filters, Feature Maps and Receptive Field · CNN: Hyperparameters · CNN- Network Layers · CNN: Architecture and Data Augmentation · Convolution from scratch · Pooling from scratch · Convolution in Pytorch · Pooling in Pytorch · CNN Image Classification: Simple CNN implementation · CNN Image Classification: Data preprocessing and Dataloaders · CNN Image Classification: Model Training
- Week 412 lecturesFamous CNN Architectures · Transfer Learning · Image Classification with Transfer Learning: Introduction · Image Classification with Transfer Learning: Dataset Accumulation · Image Classification with Transfer Learning: Data Exploration · Image Classification with Transfer Learning: Data Preprocessing · Image Classification with Transfer Learning: Pretrained Model Usage · Image Classification with Transfer Learning: Training and Evaluation · Image Segmentation: Introduction to U-Net · Image Segmentation: Data Accumulation · Image Segmentation: Data Preprocessing · Image Segmentation: Training and Evaluation
- Week 56 lecturesOptimization in DL -- Challenges · Convex Optimization · Gradient Descent Algorithm · Accelerating Gradient Descent · Gradient Descent Demo · Accelerating Gradient Descent -- Demo
- Week 68 lecturesIntroduction to Generative Models · Introduction to Latent Space · Types of Generative Models · Introduction to Generative Adversarial Networks · Fashion MNIST Generation using GAN: Overview and Data Processing · Fashion MNIST Generation using GAN: Model Implementation · Fashion MNIST Generation using GAN: Adversarial Training Loop · Fashion MNIST Generation using GAN: Model Evaluation
- Week 75 lecturesIntroduction to Sequence Modeling · Mathematical Modeling of Sequences · Language Modelling · Recurrent Neural Networks · Vanilla RNN : Python Demo
- Week 84 lecturesMemory Based RNN models · Bidirectional RNN · Encoder-Decoder Architectures · Sentiment Analysis : Demo
- Week 94 lecturesWord Embeddings · The Attention Mechanism · Neural Machine Translation -- Overview · Neural Machine Translation -- Code Walkthrough
- Week 105 lecturesFrom RNN Attention to Transformer Attention · Parts of the Transformer Architecture · The Original Transformer · From Transformers to LLMs · Modern Transformers in Language Models
- Week 118 lecturesVision Transformers · Demo -- NLP tasks through Hugging Face and API calls · Introduction to Generative for Images · Evaluation Metrics for Image Generation · Introduction to Denoising Diffusion Probabilistic Models (DDPM) · Training DDPM · Denoising Diffusion Implicit Models DDIM · Controlled conditioned generation and Latent Diffusion
- Week 129 lecturesCustom Tokenization and Data preprocessing · Self-Attention Head · Multi-Head Self Attention · GPT Language Model · Retrieval Augmented Generation(RAG) · LLM Finetuning: Model Initialization · LLM Finetuning: Data Preparation · LLM Finetuning: Data Generation via LLM · LLM Finetuning: Updating the weights via LoRA