6.
Deployment
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Dive into cheap deep learning
Table Of Contents
Getting Started
1. Introduction
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1.1. time
1.2. 技术
1.3. 隐私
1.4. money
1.5. Data
2. Lightweight
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2.1. Lightweight
2.2. SqueezeNet
2.3. MobileNet
2.4. MobileNet-v2
2.5. ShuffleNet
2.6. GhostNet
3. Compression
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3.1. 模型压缩
3.2. 参数剪枝(Pruning)
3.3. Knowledge-Distillation
3.4. 量化
4. Write code
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4.1. Jupyter
4.2. API
5. Train
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5.1. Server
5.2. Active Learning
5.3. Pretrain
5.4. 改进
5.5. 结构
6. Deployment
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6.1. 芯片
6.2. Edge
6.3. mobile
6.4. MCU
6.5. AI 中台
Dive into cheap deep learning
Table Of Contents
Getting Started
1. Introduction
keyboard_arrow_down
1.1. time
1.2. 技术
1.3. 隐私
1.4. money
1.5. Data
2. Lightweight
keyboard_arrow_down
2.1. Lightweight
2.2. SqueezeNet
2.3. MobileNet
2.4. MobileNet-v2
2.5. ShuffleNet
2.6. GhostNet
3. Compression
keyboard_arrow_down
3.1. 模型压缩
3.2. 参数剪枝(Pruning)
3.3. Knowledge-Distillation
3.4. 量化
4. Write code
keyboard_arrow_down
4.1. Jupyter
4.2. API
5. Train
keyboard_arrow_down
5.1. Server
5.2. Active Learning
5.3. Pretrain
5.4. 改进
5.5. 结构
6. Deployment
keyboard_arrow_down
6.1. 芯片
6.2. Edge
6.3. mobile
6.4. MCU
6.5. AI 中台
6.1.
芯片
¶
移动硬件上跑深度学习，于是 MIT 的 Viviene Sze 发表了第一款深度学习加速芯片 Eyeriss.
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6. Deployment
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6.2. Edge