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d2l-learning's Introduction

大纲

深度学习基础

  1. 线性回归
  2. Softmax回归
  3. 多层感知机
  4. 模型选择
  5. 过拟合, 欠拟合
  6. 权重衰退
  7. Dropout
  8. 数值稳定性
  9. 模型初始化和激活函数

卷积神仙网络

  1. LeNet
  2. AlexNet
  3. VGG
  4. NiN
  5. GoogLeNet
  6. ResNet

计算机视觉

  1. 图片增广
  2. 微调
  3. R-CNN, SSD, YOLO
  4. FCN
  5. 样式迁移

循环神经网络

  1. RNN, GRU, LSTM
  2. 深层, 双向RNN
  3. Seq2Seq

注意力机制

  1. Seq2Seq + Attention
  2. Transformer
  3. BERT

性能

  1. CPU, GPU, TPU
  2. 使用(多)GPU
  3. 分布式

应用

  1. 房价预测
  2. 图片分类
  3. 物体检测
  4. 语义分割
  5. BERT
  6. 样式迁移
  7. 语言模型
  8. 机器翻译
  9. 自然语言推理

没有讲到的应用, 模型

计算机视觉

  1. 人脸识别
  2. 体态识别
  3. 无人驾驶
  4. 图片合成
  5. 超分辨率
  6. 医学图片

自然语言处理

  1. 文本分类
  2. 文本合成
  3. 文本摘要
  4. 实体命名识别

图神经网络

语音识别

时序数据

玩游戏

代码生成

音乐

推荐系统

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