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Machine Learning/Algorithm
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TinyML and Efficient Deep Learning Computing MIT 6.S965 Fall 2022
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Distributed Training and Gradient Compression (Part I)
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Distributed Training and Gradient Compression (Part I)
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Playlist
Chapters
Chapter 1:Basics of Neural Networks
Chapter 2:Training Deep Nets with PyTorch
Chapter 3:Pruning and Sparsity (Part I)
Chapter 4:Pruning and Sparsity (Part II)
Chapter 5:Pruning and Sparsity (Part II)
Chapter 6:Quantization (Part I)
Chapter 7:Quantization (Part I)
Chapter 8:Quantization (Part II)
Chapter 9:Neural Architecture Search (Part I)
Chapter 10:Neural Architecture Search (Part II)
Chapter 11:Neural Architecture Search (Part II)
Chapter 12:Neural Architecture Search (Part III)
Chapter 13:Knowledge Distillation
Chapter 14:Knowledge Distillation
Chapter 15:MCUNet- Tiny Neural Network Design for Microcontrollers
Chapter 16:MCUNet- Tiny Neural Network Design for Microcontrollers
Chapter 17:Distributed Training and Gradient Compression (Part I)
Chapter 18:Distributed Training and Gradient Compression (Part I)
Chapter 19:Distributed Training and Gradient Compression (Part II)
Chapter 20:Distributed Training and Gradient Compression (Part II)
Chapter 21:On-Device Training and Transfer Learning (Part I)
Chapter 22:On-Device Training and Transfer Learning (Part I)
Chapter 23:On-Device Training and Transfer Learning (Part II)
Chapter 24:On-Device Training and Transfer Learning (Part II)
Chapter 25:TinyEngine - Efficient Training and Inference on Microcontrollers
Chapter 26:TinyEngine - Efficient Training and Inference on Microcontrollers
Chapter 27:Efficient Point Cloud Recognition
Chapter 28:Efficient Point Cloud Recognition
Chapter 29:Efficient Video Understanding and Generative Models
Chapter 30:Efficient Video Understanding and Generative Models
Chapter 31:Efficient Transformers
Chapter 32:Efficient Transformers
Chapter 33:Basics of Quantum Computing
Chapter 34:Basics of Quantum Computing
Chapter 35:Quantum Machine Learning
Chapter 36:Noise-Robust Quantum Machine Learning
Chapter 37:Course Summary
Chapter 38:Course Summary
Chapter 39:AI Model EfficiencyToolkit (AIMET)
Chapter 40:AI Model EfficiencyToolkit (AIMET)
Duration:1 hours and 1 minutes and 5 seconds
Date:2023/07/29
Uploader:桂花蒸
Introduction
keywords:
tinyML
TinyML and Efficient Deep Learning Computing MIT 6.S965 Fall 2022
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