TinyML and Efficient Deep Learning Computing MIT 6.S965 Fall 2022

TinyML and Efficient Deep Learning Computing  MIT 6.S965 Fall 2022

• Chapter 1：Basics of Neural Networks (1 hours and 1 minutes and 31 seconds)

• Chapter 2：Training Deep Nets with PyTorch (18 minutes and 54 seconds)

• Chapter 3：Pruning and Sparsity (Part I) (1 hours and 7 minutes and 6 seconds)

• Chapter 4：Pruning and Sparsity (Part II) (1 hours and 7 minutes and 45 seconds)

• Chapter 5：Pruning and Sparsity (Part II) (1 hours and 7 minutes and 39 seconds)

• Chapter 6：Quantization (Part I) (1 hours and 11 minutes and 45 seconds)

• Chapter 7：Quantization (Part I) (1 hours and 11 minutes and 42 seconds)

• Chapter 8：Quantization (Part II) (1 hours and 10 minutes and 53 seconds)

• Chapter 9：Neural Architecture Search (Part I) (1 hours and 4 minutes and 0 seconds)

• Chapter 10：Neural Architecture Search (Part II) (1 hours and 12 minutes and 54 seconds)

• Chapter 11：Neural Architecture Search (Part II) (1 hours and 12 minutes and 50 seconds)

• Chapter 12：Neural Architecture Search (Part III) (1 hours and 6 minutes and 57 seconds)

• Chapter 13：Knowledge Distillation (1 hours and 7 minutes and 25 seconds)

• Chapter 14：Knowledge Distillation (1 hours and 7 minutes and 21 seconds)

• Chapter 15：MCUNet- Tiny Neural Network Design for Microcontrollers (1 hours and 6 minutes and 41 seconds)

• Chapter 16：MCUNet- Tiny Neural Network Design for Microcontrollers (1 hours and 6 minutes and 38 seconds)

• Chapter 17：Distributed Training and Gradient Compression (Part I) (1 hours and 1 minutes and 19 seconds)

• Chapter 18：Distributed Training and Gradient Compression (Part I) (1 hours and 1 minutes and 5 seconds)

• Chapter 19：Distributed Training and Gradient Compression (Part II) (57 minutes and 32 seconds)

• Chapter 20：Distributed Training and Gradient Compression (Part II) (57 minutes and 28 seconds)

• Chapter 21：On-Device Training and Transfer Learning (Part I) (1 hours and 15 minutes and 57 seconds)

• Chapter 22：On-Device Training and Transfer Learning (Part I) (1 hours and 15 minutes and 55 seconds)

• Chapter 23：On-Device Training and Transfer Learning (Part II) (1 hours and 6 minutes and 16 seconds)

• Chapter 24：On-Device Training and Transfer Learning (Part II) (1 hours and 6 minutes and 29 seconds)

• Chapter 25：TinyEngine - Efficient Training and Inference on Microcontrollers (1 hours and 15 minutes and 6 seconds)

• Chapter 26：TinyEngine - Efficient Training and Inference on Microcontrollers (1 hours and 15 minutes and 1 seconds)

• Chapter 27：Efficient Point Cloud Recognition (1 hours and 14 minutes and 53 seconds)

• Chapter 28：Efficient Point Cloud Recognition (1 hours and 14 minutes and 13 seconds)

• Chapter 29：Efficient Video Understanding and Generative Models (1 hours and 37 minutes and 46 seconds)

• Chapter 30：Efficient Video Understanding and Generative Models (1 hours and 37 minutes and 40 seconds)

• Chapter 31：Efficient Transformers (1 hours and 18 minutes and 8 seconds)

• Chapter 32：Efficient Transformers (1 hours and 18 minutes and 4 seconds)

• Chapter 33：Basics of Quantum Computing (38 minutes and 38 seconds)

• Chapter 34：Basics of Quantum Computing (38 minutes and 38 seconds)

• Chapter 35：Quantum Machine Learning (1 hours and 9 minutes and 1 seconds)

• Chapter 36：Noise-Robust Quantum Machine Learning (1 hours and 8 minutes and 57 seconds)

• Chapter 37：Course Summary (12 minutes and 47 seconds)

• Chapter 38：Course Summary (12 minutes and 49 seconds)

• Chapter 39：AI Model EfficiencyToolkit (AIMET) (45 minutes and 15 seconds)

• Chapter 40：AI Model EfficiencyToolkit (AIMET) (45 minutes and 12 seconds)