课时1：Basics of Neural Networks

课时2：Training Deep Nets with PyTorch

课时3：Pruning and Sparsity (Part I)

课时4：Pruning and Sparsity (Part II)

课时5：Pruning and Sparsity (Part II)

课时6：Quantization (Part I)

课时7：Quantization (Part I)

课时8：Quantization (Part II)

课时9：Neural Architecture Search (Part I)

课时10：Neural Architecture Search (Part II)

课时11：Neural Architecture Search (Part II)

课时12：Neural Architecture Search (Part III)

课时13：Knowledge Distillation

课时14：Knowledge Distillation

课时15：MCUNet- Tiny Neural Network Design for Microcontrollers

课时16：MCUNet- Tiny Neural Network Design for Microcontrollers

课时17：Distributed Training and Gradient Compression (Part I)

课时18：Distributed Training and Gradient Compression (Part I)

课时19：Distributed Training and Gradient Compression (Part II)

课时20：Distributed Training and Gradient Compression (Part II)

课时21：On-Device Training and Transfer Learning (Part I)

课时22：On-Device Training and Transfer Learning (Part I)

课时23：On-Device Training and Transfer Learning (Part II)

课时24：On-Device Training and Transfer Learning (Part II)

课时25：TinyEngine - Efficient Training and Inference on Microcontrollers

课时26：TinyEngine - Efficient Training and Inference on Microcontrollers

课时27：Efficient Point Cloud Recognition

课时28：Efficient Point Cloud Recognition

课时29：Efficient Video Understanding and Generative Models

课时30：Efficient Video Understanding and Generative Models

课时31：Efficient Transformers

课时32：Efficient Transformers

课时33：Basics of Quantum Computing

课时34：Basics of Quantum Computing

课时35：Quantum Machine Learning

课时36：Noise-Robust Quantum Machine Learning

课时37：Course Summary

课时38：Course Summary

课时39：AI Model EfficiencyToolkit (AIMET)

课时40：AI Model EfficiencyToolkit (AIMET)