CS231n: Convolutional Neural Network for Visual Recognition基于CNN的视觉识别课程
文章目录
- CS231n: Convolutional Neural Network for Visual Recognition
- lecture01：Introduction, A brief history of computer vision
- lecture02：Image Classification pipeline
- lecture03: Loss Functions and Optimization
- lecture04: Neural Network and Back propagation
- lecture05: Convolutional Neural Network
- lecture06: Hardware and software
- lecture07: Training Neural Networks I
- lecture08: Training Neural Networks II
- lecture09: CNN Architectures Transfer Leaning
- lecture10: RNN Recurrent Neural Network
- lecture11: Generative Models
- lecture12: Detection and Segmentation
- lecture13: Visualizing and Understanding
- lecture14: Reinforcement Learning
- lecture15: Project Design
- Knowledge Based
课程教学大纲：syllabus
CS231n: Convolutional Neural Network for Visual Recognition
以下为全部内容，从第十课RCNN后没有笔记
lecture01：Introduction, A brief history of computer vision
- Computer vision overview
- Historical context
- Course logistics
lecture02：Image Classification pipeline
-
The data-driven approach
-
K-nearest neighbor
-
Linear classification SVM, Softmax
-
Two-layer neural network
-
Image features
lecture03: Loss Functions and Optimization
-
Linear classification II
-
Higher-level representations, image features
-
Optimization, stochastic gradient descent
lecture04: Neural Network and Back propagation
-
Backpropagation
-
Multi-layer Perceptrons
-
The neural viewpoint
lecture05: Convolutional Neural Network
- History
- Convolution and pooling
- ConvNets outside vision
- CS231n课程笔记翻译：卷积神经网络笔记 - 猴子的文章 - 知乎专栏
lecture06: Hardware and software
- CPUs, GPUs, TPUs
- PyTorch, TensorFlow
- Dynamic vs Static computation graphs
lecture07: Training Neural Networks I
- Update rules, ensembles, data augmentation, transfer learning
lecture08: Training Neural Networks II
- Update rules, ensembles, data augmentation, transfer learning
lecture09: CNN Architectures Transfer Leaning
- AlexNet, VGG, GoogLeNet, ResNet, etc
lecture10: RNN Recurrent Neural Network
- RNN, LSTM, GRU
- Language modeling
- Image captioning, visual question answering
- Soft attention
lecture11: Generative Models
lecture12: Detection and Segmentation
lecture13: Visualizing and Understanding
- Feature visualization and inversion
- Adversarial examples
- DeepDream and style transfer
lecture14: Reinforcement Learning
- Policy gradients, hard attention
- Q-Learning, Actor-Critic
lecture15: Project Design
CS231n课程笔记翻译：Python Numpy教程 - 智能单元 - 知乎专栏
斯坦福CS231n课程作业# 1简介 - 智能单元 - 知乎专栏
斯坦福CS231n课程作业# 2简介 - 智能单元 - 知乎专栏
斯坦福CS231n课程作业# 3简介 - 智能单元 - 知乎专栏
Knowledge Based
Deep Learning 学习笔记（一）：softmax Regression及Python实现
来源CS231n官方笔记授权翻译总集篇发布 - 智能单元 - 知乎专栏
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