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TinyML and Efficient Deep Learning Computing MIT 6.S965 Fall 2022
共40课时 1天18小时26分10秒秒
简介
TinyML and Efficient Deep Learning Computing MIT 6.S965 Fall 2022
章节
- 课时1：Basics of Neural Networks (1小时1分31秒)
- 课时2：Training Deep Nets with PyTorch (18分54秒)
- 课时3：Pruning and Sparsity (Part I) (1小时7分6秒)
- 课时4：Pruning and Sparsity (Part II) (1小时7分45秒)
- 课时5：Pruning and Sparsity (Part II) (1小时7分39秒)
- 课时6：Quantization (Part I) (1小时11分45秒)
- 课时7：Quantization (Part I) (1小时11分42秒)
- 课时8：Quantization (Part II) (1小时10分53秒)
- 课时9：Neural Architecture Search (Part I) (1小时4分0秒)
- 课时10：Neural Architecture Search (Part II) (1小时12分54秒)
- 课时11：Neural Architecture Search (Part II) (1小时12分50秒)
- 课时12：Neural Architecture Search (Part III) (1小时6分57秒)
- 课时13：Knowledge Distillation (1小时7分25秒)
- 课时14：Knowledge Distillation (1小时7分21秒)
- 课时15：MCUNet- Tiny Neural Network Design for Microcontrollers (1小时6分41秒)
- 课时16：MCUNet- Tiny Neural Network Design for Microcontrollers (1小时6分38秒)
- 课时17：Distributed Training and Gradient Compression (Part I) (1小时1分19秒)
- 课时18：Distributed Training and Gradient Compression (Part I) (1小时1分5秒)
- 课时19：Distributed Training and Gradient Compression (Part II) (57分32秒)
- 课时20：Distributed Training and Gradient Compression (Part II) (57分28秒)
- 课时21：On-Device Training and Transfer Learning (Part I) (1小时15分57秒)
- 课时22：On-Device Training and Transfer Learning (Part I) (1小时15分55秒)
- 课时23：On-Device Training and Transfer Learning (Part II) (1小时6分16秒)
- 课时24：On-Device Training and Transfer Learning (Part II) (1小时6分29秒)
- 课时25：TinyEngine - Efficient Training and Inference on Microcontrollers (1小时15分6秒)
- 课时26：TinyEngine - Efficient Training and Inference on Microcontrollers (1小时15分1秒)
- 课时27：Efficient Point Cloud Recognition (1小时14分53秒)
- 课时28：Efficient Point Cloud Recognition (1小时14分13秒)
- 课时29：Efficient Video Understanding and Generative Models (1小时37分46秒)
- 课时30：Efficient Video Understanding and Generative Models (1小时37分40秒)
- 课时31：Efficient Transformers (1小时18分8秒)
- 课时32：Efficient Transformers (1小时18分4秒)
- 课时33：Basics of Quantum Computing (38分38秒)
- 课时34：Basics of Quantum Computing (38分38秒)
- 课时35：Quantum Machine Learning (1小时9分1秒)
- 课时36：Noise-Robust Quantum Machine Learning (1小时8分57秒)
- 课时37：Course Summary (12分47秒)
- 课时38：Course Summary (12分49秒)
- 课时39：AI Model EfficiencyToolkit (AIMET) (45分15秒)
- 课时40：AI Model EfficiencyToolkit (AIMET) (45分12秒)
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