Advanced ML Methods for Edge AI (2/2)

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Week 1 - Basics and History of Neural Networks
- A Concise History of Neural Networks (1/4)
- Welcome to Week 1
- A Concise History of Neural Networks (2/4)
- Course Start Survey
- A Concise History of Neural Networks (3/4)
- A Concise History of Neural Networks (4/4)
- Self-Reflection Questionnaire
- Stochastic Gradient Descent
- Computational Graph
- Convolutional Neural Networks
- Self-Reflection Questionnaire
- Course Introduction
- A Concise History of Neural Networks (1/4)
- A Concise History of Neural Networks (2/4)
- A Concise History of Neural Networks (3/4)
- A Concise History of Neural Networks (4/4)
- Stochastic Gradient Descent
- Computational Graph
- Convolutional Neural Networks
- Learning Material
- Weekly Assignment
- Practical Exercise Week 1 - LENGTH
- Practical Exercise Quiz
- Practical Exercise (Bonus)
- Practical Exercise Week 1 - Solution
- Self-Reflection Questionnaire
- Self-Reflection Questionnaire
Week 2 - CNN Architectures
- Welcome to Week 2
- Self-Reflection Questionnaire
- Self-Reflection Questionnaire
- CNN Architectures (1/6)
- CNN Architectures (1/6)
- CNN Architectures (2/6)
- CNN Architectures (2/6)
- CNN Architectures (3/6)
- CNN Architectures (3/6)
- CNN Architectures (4/6)
- CNN Architectures (4/6)
- CNN Architectures (5/6)
- CNN Architectures (5/6)
- CNN Architectures (6/6)
- CNN Architectures (6/6)
- Transformer
- Transformer
- Learning Material
- Weekly Assignment
- Practical Exercise Week 2 - CIFAR 100
- Practical Exercise Quiz
- Practical Exercise Week 2 - Solution
- Self-Reflection Questionnaire
- Self-Reflection Questionnaire
Week 3 - Compact Network Design and Knowledge Distillation
- Neural Network Compression
- Compact Network Design 1
- Welcome to Week 3
- Neural Network Compression
- Compact Network Design 2
- Compact Network Design (1/3)
- Compact Network Design 3
- Compact Network Design (2/3)
- Knowledge Distillation 1
- Compact Network Design (3/3)
- Knowledge Distillation 2
- Knowledge Distillation (1/2)
- Weekly Assignment
- Knowledge Distillation (2/2)
- Learning Material
- Practical Exercise Week 3 - Knowledge Distillation
- Practical Exercise Quiz
- Practical Exercise Week 3 - Solution
Week 4 - Advanced Deep Compression Methods
- Pruning
- Dynamic Network
- Supernet-subnets Architecture Search
- Welcome to Week 4
- Supernet-Subnets Architecture Search
- Network Pruning
- Neural Network Quantization
- Dynamic Network
- Binary Neural Networks 1
- Binary Neural Networks 2
- Neural Network Quantization
- Weekly Assignment
- Binary Neural Networks (1/2)
- Binary Neural Networks (2/2)
- Learning Material
- Practical Exercise Week 4 - Binary Neural Networks
- Practical Exercise Quiz
- Practical Exercise Week 4 - Solution
Week 5 - Edge AI Introduction and Federated Learning
- Introduction of Edge AI
- Training and Inference
- Federated Learning 1
- Federated Learning 2
- Introduction of Edge AI
- Welcome to Week 5
- Federated Learning 3
- Training and Inference
- Weekly Assignment
- Federated Learning (1/3)
- Federated Learning (2/3)
- Federated Learning (3/3)
- Learning Material
- Practical Exercise Week 5 - Federated Learning
- Practical Exercise Quiz
- Practical Exercise Week 5 - Solution
Week 6 - Edge AI Concepts
- Edge AI Computing Model
- Advanced ML Methods for Edge AI 1
- Advanced ML Methods for Edge AI 2
- Edge AI Hardware
- Welcome to Week 6
- Application Scenarios
- Edge AI Computing Model
- Advanced ML Methods for Edge AI (1/2)
- Weekly Assignment
- Advanced ML Methods for Edge AI (2/2)
- Edge AI Hardware
- Edge AI Application Scenarios
- Learning Material
- Practical Exercise Week 6 - Collaborative Inference
- Practical Exercise Quiz
- Practical Exercise Week 6 - Solution
Week 7 - Final Exam
- Final Exam
- Final exam
I like, I wish
- I like, I wish - what is that?