COL775: Difference between revisions
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| credit_structure = 3-0-2 | | credit_structure = 3-0-2 | ||
| pre_requisites = Any one of ELL 409/ELL 774 / COL 341/ COL | | pre_requisites = Any one of ELL 409/ELL 774 / COL 341/ COL | ||
| overlaps = AIL721, APL745 | | overlaps = [[AIL721]], [[APL745]] | ||
}} | }} | ||
== COL775 : Deep Learning == | == COL775 : Deep Learning == | ||
333/ COL 671 Basics: Introduction, Why Deep Learning, Multi-layered Perceptron, Neural Networks as Universal Function Approximators, Backpropagation, Regularization, Ll-L2 Norms, Early Stopping, Dropouts. Optimization: Stochastic Gradient Descent, First-order and second order methods, Algorithms such as RMSProp, Adams, AdaGrad. Other Topics on Advanced Optimization. Convolutional Networks (CNNs) - kernels, pooling operations, Applications to Computer Vision. Recurrent Neural Networks, LSTMs, Attention, Transformers, Language models: BERT, GPT2 etc. Applications in NLP. Generative Models: Variational Auto-encoders, Generative Adversarial Networks (GANs). Graph Convolutional Networks, Graph Attention Networks, and variations. Deep Reinforcement Learning - basics of (Deep) RL, More Advanced topics such as visual question answering, Neuro-symbolic reasoning, self-supervised learning, Explainability and Fairness, Domain Adaptation etc. | 333/ COL 671 Basics: Introduction, Why Deep Learning, Multi-layered Perceptron, Neural Networks as Universal Function Approximators, Backpropagation, Regularization, Ll-L2 Norms, Early Stopping, Dropouts. Optimization: Stochastic Gradient Descent, First-order and second order methods, Algorithms such as RMSProp, Adams, AdaGrad. Other Topics on Advanced Optimization. Convolutional Networks (CNNs) - kernels, pooling operations, Applications to Computer Vision. Recurrent Neural Networks, LSTMs, Attention, Transformers, Language models: BERT, GPT2 etc. Applications in NLP. Generative Models: Variational Auto-encoders, Generative Adversarial Networks (GANs). Graph Convolutional Networks, Graph Attention Networks, and variations. Deep Reinforcement Learning - basics of (Deep) RL, More Advanced topics such as visual question answering, Neuro-symbolic reasoning, self-supervised learning, Explainability and Fairness, Domain Adaptation etc. | ||
Latest revision as of 16:26, 14 April 2026
| COL775 | |
|---|---|
| Deep Learning | |
| Credits | 4 |
| Structure | 3-0-2 |
| Pre-requisites | Any one of ELL 409/ELL 774 / COL 341/ COL |
| Overlaps | AIL721, APL745 |
COL775 : Deep Learning
333/ COL 671 Basics: Introduction, Why Deep Learning, Multi-layered Perceptron, Neural Networks as Universal Function Approximators, Backpropagation, Regularization, Ll-L2 Norms, Early Stopping, Dropouts. Optimization: Stochastic Gradient Descent, First-order and second order methods, Algorithms such as RMSProp, Adams, AdaGrad. Other Topics on Advanced Optimization. Convolutional Networks (CNNs) - kernels, pooling operations, Applications to Computer Vision. Recurrent Neural Networks, LSTMs, Attention, Transformers, Language models: BERT, GPT2 etc. Applications in NLP. Generative Models: Variational Auto-encoders, Generative Adversarial Networks (GANs). Graph Convolutional Networks, Graph Attention Networks, and variations. Deep Reinforcement Learning - basics of (Deep) RL, More Advanced topics such as visual question answering, Neuro-symbolic reasoning, self-supervised learning, Explainability and Fairness, Domain Adaptation etc.