Have you found it difficult to deploy neural networks on mobile devices and IoT devices? Have you ever found it too slow to train neural networks? This course is a deep dive into efficient machine learning techniques that enable powerful deep learning applications on resource-constrained devices. Topics cover efficient inference techniques, including model compression, pruning, quantization, neural architecture search, and distillation; and efficient training techniques, including gradient compression and on-device transfer learning; followed by application-specific model optimization techniques for videos, point cloud, and NLP; and efficient quantum machine learning. Students will get hands-on experience implementing deep learning applications on microcontrollers, mobile phones, and quantum machines with an open-ended design project related to mobile AI.

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

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