Learn · AI driven Perception, Learning and Mapping for Drones
Lecture 23 of 48Week 5 Foundations of Neural Networks
Math for AI - 6
Prof. Suresh Sundaram and Prof. Aniruddh Sikdar · IISc Bangalore
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- AI driven Perception, Learning and Mapping for Drones, IISc Bangalore (at NPTEL)
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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All the lectures
- Introduction to the course
- Introduction - 1
- Introduction -2
- Foundations in Neural Networks: Basic Concepts - 1
- Foundations in Neural Networks: Basic Concepts - 2
- Foundations of Neural Networks - Single Neuron Model - 1
- Foundations of Neural Networks - Single Neuron Model - 2
- Foundations of Neural Networks - Single Neuron Model - 3
- Foundations of Neural Networks - Single Neuron Model - 4
- Math for AI - 1
- Training Parameters: Part 1
- Training Parameters : Part 2
- Loss Functions
- Math for AI - 2
- Math for AI - 3
- Radial Basis functions
- Introduction to pytorch
- Math for AI - 4
- Math for AI - 5
- Introduction to CNNs - 1
- Introduction to CNNs - 2
- Foundation of Convolutional Neural Networks
- Math for AI - 6
- Tutorial on pytorch - 2
- Normalization techniques
- Feature visualization
- Popular CNN backbones
- Tutorial on pytorch - 3
- Math for AI - 7
- Sequential Learning with Feedback Networks and MNNs
- Sequential Learning with RNNs and LSTMs
- Sequential Learning with Attention mechanism
- Object detection-1
- Object detection-2
- Introduction to Semantic Segmentation - 1
- Semantic Segmentation-2
- Segmentation task-3
- Advanced Topics in Semantic Segmentation - 4
- Compressing and Structuring Representations: Knowledge Distillation and Contrastive Learning
- Learning Beyond Static, Single-Modal Data – 1
- Introduction to Generative modeling and Generative adversarial Networks
- Tutorial 1: Generative adversarial Networks
- Tutorial 2: Generative adversarial Networks
- Reinforcement Learning - Part 1: Foundations of Conventional RL
- Reinforcement Learning - Part 2: Deep RL
- Reinforcement Learning - Part 3: Deep RL Demo
- Sensor denied navigation, localication and mapping
- Distributed AI