5.
Train
navigate_next
5.5.
结构
search
Quick search
code
Show Source
PDF
Github
Dive into cheap deep learning
Table Of Contents
Getting Started
1. Introduction
1.1. time
1.2. 技术
1.3. 隐私
1.4. money
1.5. Data
2. Lightweight
2.1. Lightweight
2.2. SqueezeNet
2.3. MobileNet
2.4. MobileNet-v2
2.5. ShuffleNet
2.6. GhostNet
3. Compression
3.1. 模型压缩
3.2. 参数剪枝(Pruning)
3.3. Knowledge-Distillation
3.4. 量化
4. Write code
4.1. Jupyter
4.2. API
5. Train
5.1. Server
5.2. Active Learning
5.3. Pretrain
5.4. 改进
5.5. 结构
6. Deployment
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
1.1. time
1.2. 技术
1.3. 隐私
1.4. money
1.5. Data
2. Lightweight
2.1. Lightweight
2.2. SqueezeNet
2.3. MobileNet
2.4. MobileNet-v2
2.5. ShuffleNet
2.6. GhostNet
3. Compression
3.1. 模型压缩
3.2. 参数剪枝(Pruning)
3.3. Knowledge-Distillation
3.4. 量化
4. Write code
4.1. Jupyter
4.2. API
5. Train
5.1. Server
5.2. Active Learning
5.3. Pretrain
5.4. 改进
5.5. 结构
6. Deployment
6.1. 芯片
6.2. Edge
6.3. mobile
6.4. MCU
6.5. AI 中台
5.5.
结构
¶
结构复用
5.5.1.
多用卷积核Versatile Filters (NeurIPS 2018)
¶
5.5.2.
乐高卷积核
¶
Table Of Contents
5.5. 结构
5.5.1. 多用卷积核Versatile Filters (NeurIPS 2018)
5.5.2. 乐高卷积核
Previous
5.4. 改进
Next
6. Deployment