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斯坦福李飞飞深度学习课程的课后作业,有3个部分Assignment #1: Image Classification, kNN, SVM, Softmax, Neural NetworkAssignment #2: Fully-Connected Nets, Batch Normalization, Dropout, Convolutional NetsAssignment #3: Image Captioning with Vanilla RNNs, Image Captioning with LSTMs, Network Visualization, Style Transfer, Generative Adversarial Networks官方资源(讲义、作业等)地址:http…

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CS231n

斯坦福李飞飞深度学习课程的课后作业,有3个部分

  1. Assignment #1: Image Classification, kNN, SVM, Softmax, Neural Network
  2. Assignment #2: Fully-Connected Nets, Batch Normalization, Dropout, Convolutional Nets
  3. Assignment #3: Image Captioning with Vanilla RNNs, Image Captioning with LSTMs, Network Visualization, Style Transfer, Generative Adversarial Networks

进度介绍:完成了Assigment1的所有内容,Assigment2中除了PyTorch.ipynb以及tensorflow的最后一个内容,其他的都完成了,Assigment3没有做。

文件介绍:

  1. 后缀为ipynb的文件是作业的主要文件,有作业的主要流程、问题和讲解
  2. cs231n\classifiers目录下的文件是算法代码的实现部分
  3. 通过dataset文件夹中的get_datasets.sh文件获取数据,就可以运行程序了
  1. 官方资源(讲义、作业等)地址
  2. 网易视频课程地址

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斯坦福李飞飞深度学习课程的课后作业,有3个部分Assignment #1: Image Classification, kNN, SVM, Softmax, Neural NetworkAssignment #2: Fully-Connected Nets, Batch Normalization, Dropout, Convolutional NetsAssignment #3: Image Captioning with Vanilla RNNs, Image Captioning with LSTMs, Network Visualization, Style Transfer, Generative Adversarial Networks官方资源(讲义、作业等)地址:http…

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