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BSDA2001 - Introduction to DL and GenAI
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BSDA2001 - Introduction to DL and GenAI
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| Field | Value |
|---|---|
| Course Code | BSDA2001 |
| Level | Diploma Level Course |
| Credits | 4 |
| Type | Data Science |
| Pre-requisites | None |
📖 Description
This course aims to provide a comprehensive introduction to the foundational and practical aspects of Deep Learning and Generative AI. Through a balanced blend of theoretical concepts and hands-on experience, students will learn to build, train, and evaluate artificial neural networks for a variety of tasks in computer vision and natural language processing. The course covers key architectures such as Convolutional Neural Networks (CNNs) for image data, Recurrent Neural Networks (RNNs) and LSTMs for sequential data, and extends into the realm of generative models including Autoencoders, Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs) and Large Language Models (LLMs).
By the end of the course, learners will gain the skills to implement core deep learning models and apply generative AI techniques to solve practical problems.
👨🏫 Instructor(s)
- the end of the course
- learners will gain the skills to implement core deep learning models and apply generative AI techniques to solve practical problems.
🗓️ Weekly Syllabus
| Week | Topic |
|---|---|
| Week 1 | Artificial Neural Networks - Theory |
| Introduction to Deep Learning, Artificial neurons, neural networks, layers, Activation functions and loss metrics | |
| Week 2 | Artificial Neural Networks - Practice |
| Hands-on: Build simple neural networks using TensorFlow/Keras, Experimentation with activation functions and opt | |
| Week 3 | Modeling Vision — CNN - Theory |
| Introduction to Convolutional Neural Networks (CNNs), CNN architecture basics, convolution and pooling layers | |
| Week 4 | Modeling Vision — CNN - Practice |
| Hands-on: CNN-based image classification (e.g., MNIST, CIFAR-10) | |
| Week 5 | Modeling Sequential Data - Theory |
| Sequence models, Recurrent Neural Networks (RNNs), LSTMs | |
| Week 6 | Modeling Sequential Data - Practice |
| Hands-on: Sentiment analysis with LSTM, Text generation with simple RNN/LSTM models, Time series prediction tasks | |
| Week 7 | Generative AI for vision —Variational AutoEncoders and GANs - Theory |
| Introduction to Generative AI, Autoencoders and Variational Autoencoders (VAEs) b | |
| Week 8 | Generative AI for vision—Diffusion Models |
| Diffusion Probabilistic models, training and inference | |
| Week 9 | Generative AI for vision— Practice |
| GANs and Pretrained Diffusion Models to generate Images | |
| Week 10 | Large Language Models —Transformer Architecture |
| Word Embeddings, Tokenization, Attention | |
| Week 11 | Large Language Models - encoder, decoder, encoder decoder models |
| BERT like models for NLP tasks, Decoders for Text Generation, Machine Translation, Fi | |
| Week 12 | Large Language Models - Practice |
| Prompting Techniques, Prompt Fine-tuning and other methods |
📝 About the Instructors
Prof. Balaji Srinivasan
Professor,
Department of Mechanical Engineering, Wadhwani School of AI ,
IIT Madras
Balaji Srinivasan is a Professor at the Wadhwani School of AI and Dept. of Mechanical Engineering at IIT-Madras. He has a PhD from Stanford (2005), MS from Purdue, B.Tech from IITM. His current research interests are in Scientific Machine Learning, Numerical solution of PDEs and Applied Deep Learning.
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Other courses by the same instructor:
BSDA2001P -
Introduction to DL and GenAI Project
Prof. Ganapathy Krishnamurthi
Professor,
Wadhwani School of AI,
IIT Madras
Ganapathy Krishnamurthi is a Professor at the Wadhwani School of AI. He has a PhD from Purdue University (2008), MSc (Physics) from IITM. His current research interests are in Generative AI, and Deep learning applied to Medical Image Analysis and Medical Image Reconstruction.
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Other courses by the same instructor:
BSDA2001P -
Introduction to DL and GenAI Project