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【2036页2023大学教学课件】麻省理工学院：TinyML和高效的深度学习计算.zip
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【2036页2023大学教学课件】麻省理工学院：TinyML和高效的深度学习计算
01 Introduction
02 Basics of Neural Networks
03 Pruning and Sparsity (Part l)
04 Pruning and Sparsity (Part Il)
05 Quantization (Part l)
06 Quantization (Part II)
07 Neural Architecture Search (Part l)
08 Neural Architecture Search (Part Il)
09 Knowledge Distillation
10 MCUNet:TinyML on Microcontrollers
11 TinyEngine and Parallel Processing
12 Transformer and LLM (Part l)
13 Transformer and LLM (Part Il)
14 Vision Transformer
15 Efficient GAN,Video Understanding,and Point Cloud Recognition
16 Diffusion Model
17 Distributed Training (Part l)
18 Distributed Training (Part Il)
19 On-Device Training and Transfer Learning
20 Efhicient Fine-tuning and Prompt Engineering
21 Basics of Quantum Computing
22 Quantum Machine Learning
23 Noise Robust Quantum ML
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