【加密】JM084 – 2021Pytorch深度学习实战 [248.2G]

【加密】JM084 – 2021Pytorch深度学习实战 [248.2G]

┣━━00.资料 [181.1G]
┃ ┣━━WEEK 1 [26.4M]
┃ ┃ ┣━━启发 [14.3M]
┃ ┃ ┃ ┣━━启发（win电脑适用）.exe [14.3M]
┃ ┃ ┃ ┗━━MAC及其他操作系统走这里.txt [80B]
┃ ┃ ┣━━GPU购买指南 + PyTorch安装及环境搭建 V4.pdf [4.8M]
┃ ┃ ┣━━Lesson 1.张量（Tensor）的创建和索引.ipynb [55.7K]
┃ ┃ ┣━━Lesson 2.张量的索引、分片、合并以及维度调整.ipynb [46.6K]
┃ ┃ ┣━━Lesson 3.张量的广播和科学运算.ipynb [65.7K]
┃ ┃ ┣━━Lesson 4.张量的线性代数运算.ipynb [67.1K]
┃ ┃ ┗━━Python的安装与环境配置.pdf [7.1M]
┃ ┣━━WEEK 10-WEEK 14 CV数据包 [180.7G]
┃ ┃ ┣━━datasets [345.1M]
┃ ┃ ┃ ┣━━FashionMNIST.zip [88.4M]
┃ ┃ ┃ ┣━━omniglot-py.zip [21.8M]
┃ ┃ ┃ ┗━━SVHN.zip [235M]
┃ ┃ ┣━━datasets2 [152.9G]
┃ ┃ ┃ ┣━━ImageNet2012 [144G]
┃ ┃ ┃ ┣━━lsun-master.zip [5.3G]
┃ ┃ ┃ ┗━━VOC.zip [3.6G]
┃ ┃ ┣━━datasets3 [27.2G]
┃ ┃ ┃ ┣━━celeba.zip [21.5G]
┃ ┃ ┃ ┣━━cifar.zip [646.6M]
┃ ┃ ┃ ┣━━sbd.zip [3.1G]
┃ ┃ ┃ ┗━━sbu.zip [2G]
┃ ┃ ┣━━必须下载datasets4和datasets2中的LSUN，其他按需下载.txt [0B]
┃ ┃ ┗━━datasets4.zip [183.8M]
┃ ┣━━WEEK 11 – WEEK 14 [34.3M]
┃ ┃ ┣━━torch_receptive_field [8.2K]
┃ ┃ ┃ ┣━━__init__.py [101B]
┃ ┃ ┃ ┗━━receptive_field.py [8.1K]
┃ ┃ ┣━━16.14 – END（5月9日更新）.ipynb [68.5K]
┃ ┃ ┣━━16.7~16.13.ipynb [30K]
┃ ┃ ┣━━17.1 – 17.3.ipynb [947K]
┃ ┃ ┣━━17.4 & 17.5.ipynb [265.7K]
┃ ┃ ┣━━Lesson 16 计算机视觉开篇（下）（5月9日更新）.pdf [13.8M]
┃ ┃ ┗━━Lesson 17 深度视觉进阶（上）V7 6月16日更新.pdf [19.2M]
┃ ┣━━WEEK 2 [9.7M]
┃ ┃ ┣━━Lesson 5.基本优化思想与最小二乘法.ipynb [180.3K]
┃ ┃ ┣━━Lesson 6.动态计算图与梯度下降入门.ipynb [216.7K]
┃ ┃ ┣━━LESSON 7 认识深度学习，认识PyTorch.pdf [7.6M]
┃ ┃ ┗━━LESSON 8 单层神经网络.pdf [1.7M]
┃ ┣━━WEEK 3、4 [283.8M]
┃ ┃ ┣━━MINST-FASHION数据集 [278.6M]
┃ ┃ ┃ ┣━━FashionMNIST [134.7M]
┃ ┃ ┃ ┃ ┣━━processed [52.9M]
┃ ┃ ┃ ┃ ┃ ┣━━test.pt [7.6M]
┃ ┃ ┃ ┃ ┃ ┗━━training.pt [45.3M]
┃ ┃ ┃ ┃ ┗━━raw [81.9M]
┃ ┃ ┃ ┃ ┣━━t10k-images-idx3-ubyte [7.5M]
┃ ┃ ┃ ┃ ┣━━t10k-images-idx3-ubyte.gz [4.2M]
┃ ┃ ┃ ┃ ┣━━t10k-labels-idx1-ubyte [9.8K]
┃ ┃ ┃ ┃ ┣━━t10k-labels-idx1-ubyte.gz [5K]
┃ ┃ ┃ ┃ ┣━━train-images-idx3-ubyte [44.9M]
┃ ┃ ┃ ┃ ┣━━train-images-idx3-ubyte.gz [25.2M]
┃ ┃ ┃ ┃ ┣━━train-labels-idx1-ubyte [58.6K]
┃ ┃ ┃ ┃ ┗━━train-labels-idx1-ubyte.gz [28.8K]
┃ ┃ ┃ ┗━━creditcard.csv [143.8M]
┃ ┃ ┣━━Lesson 10 神经网络的损失函数.pdf [1M]
┃ ┃ ┣━━Lesson 11 神经网络的学习.pdf [2.6M]
┃ ┃ ┗━━Lesson 9 深层神经网络.pdf [1.5M]
┃ ┣━━WEEK 5 [968.9K]
┃ ┃ ┣━━Lesson 12.0 深度学习基础网络手动搭建与快速实现.ipynb [7.1K]
┃ ┃ ┣━━Lesson 12.1 深度学习建模实验中数据集创建函数的创建与使用.ipynb [290.8K]
┃ ┃ ┣━━Lesson 12.2 PyTorch深度学习建模可视化工具TensorBoard的安装与使用.ipynb [13.7K]
┃ ┃ ┣━━Lesson 12.3 线性回归建模实验.ipynb [32K]
┃ ┃ ┣━━Lesson 12.4 逻辑回归建模实验.ipynb [276.5K]
┃ ┃ ┣━━Lesson 12.5 softmax回归建模实验.ipynb [338.3K]
┃ ┃ ┗━━torchLearning.py [10.5K]
┃ ┣━━WEEK 6 [717.2K]
┃ ┃ ┣━━Lesson 13.1 深度学习建模目标与性能评估理论.ipynb [130.9K]
┃ ┃ ┗━━Lesson 13.2 模型拟合度概念介绍与欠拟合模型的结构调整策略.ipynb [586.3K]
┃ ┣━━WEEK 7（5月20日更新torchlearning.py [895.8K]
┃ ┃ ┣━━【加餐】损失函数的随机创建现象详解.ipynb [41.9K]
┃ ┃ ┣━━Lesson 13.3 梯度不平稳性与Glorot条件.ipynb [304.1K]
┃ ┃ ┣━━Lesson 13.4 Dead ReLU Problem与学习率优化.ipynb [169.2K]
┃ ┃ ┣━━Lesson 13.5 Xavier方法与kaiming方法（HE初始化）.ipynb [324K]
┃ ┃ ┗━━torchLearning.py [56.7K]
┃ ┣━━WEEK 8 [960.6K]
┃ ┃ ┣━━Lesson 14.1 数据归一化与Batch Normalization理论基础.ipynb [341.9K]
┃ ┃ ┣━━Lesson 14.2 Batch Normalization在PyTorch中的实现.ipynb [198.7K]
┃ ┃ ┗━━Lesson 14.3 Batch Normalization综合调参实战.ipynb [420K]
┃ ┗━━WEEK 9 & WEEK 10 [85.4M]
┃ ┣━━图像 [1.1M]
┃ ┃ ┣━━blue-peacock.jpg [825.2K]
┃ ┃ ┗━━edge detection.PNG [274.7K]
┃ ┣━━WEEK 10-WEEK 14 CV论文包 [70.3M]
┃ ┃ ┣━━从数学的角度理解卷积&卷积神经网络 [1.2M]
┃ ┃ ┃ ┣━━The Loss Surfaces of Multilayer Networks.pdf [754.4K]
┃ ┃ ┃ ┗━━understanding convolutional neural networks with a mathematical model.pdf [521K]
┃ ┃ ┣━━关于深层神经网络vs浅层神经网络的研究 [7.9M]
┃ ┃ ┃ ┣━━Comparing Shallow versus Deep Neural Network Architectures for autometic music genre classification.pdf [1M]
┃ ┃ ┃ ┣━━Deep vs. Shallow Networks_ an Approximation Theory Perspective.pdf [960.3K]
┃ ┃ ┃ ┣━━Layers_Modification_of_Convolutional_Neural_Networ.pdf [996.7K]
┃ ┃ ┃ ┣━━Learning Functions_ When Is Deep Better Than Shallow arXiv.pdf [1.1M]
┃ ┃ ┃ ┣━━On the Complexity of shallow and deep neural network classifiers.pdf [1.5M]
┃ ┃ ┃ ┣━━When and Why are Deep Networks better than shallow ones_.pdf [669.5K]
┃ ┃ ┃ ┗━━Why_and_when_can_deep-but_not_shallow-networks_avo.pdf [1.7M]
┃ ┃ ┣━━卷积神经网络的可视化 [34.6M]
┃ ┃ ┃ ┗━━Visualizing and Understanding Convolutional Networks.pdf [34.6M]
┃ ┃ ┣━━卷积神经网络的优化 [9.2M]
┃ ┃ ┃ ┣━━Comparative_Study_of_First_Order_Optimizers_for_Im.pdf [573.2K]
┃ ┃ ┃ ┣━━effects of padding on LSTM and CNNs.pdf [357.4K]
┃ ┃ ┃ ┣━━Evaluation of Pooling Operations in.pdf [284K]
┃ ┃ ┃ ┣━━Improving_the_Separability_of_Deep_Features_with_Discriminative Convolution Filters for.pdf [7.5M]
┃ ┃ ┃ ┗━━understand the effective receptive field in deep CNN.pdf [583.8K]
┃ ┃ ┣━━[1998 LeNet5 Original Paper]Gradient-Based Learning Applied to Document Recognition.pdf [4.2M]
┃ ┃ ┣━━[2012 AlexNet Original Paper]NIPS-2012-imagenet-classification-with-deep-convolutional-neural-networks-Paper.pdf [1.4M]
┃ ┃ ┣━━[2014 GoogLeNet Original Paper]Going deeper with convolutions.pdf [1.1M]
┃ ┃ ┣━━[2014 NiN Original Paper]Network in Network.pdf [550.7K]
┃ ┃ ┣━━[2014 VGG Original Paper]Very deep convolutional networks for large-scale image recognition.pdf [195.3K]
┃ ┃ ┣━━[2015 ResNet Original Paper]Deep Residual Learning for Image Recognition.pdf [800.2K]
┃ ┃ ┣━━Recent Advances in Convolutional Neural Networks.pdf [4.4M]
┃ ┃ ┣━━Striving for Simplicity The All Convolutional Net.pdf [4M]
┃ ┃ ┗━━Xception Deep Learning with Depthwise Separable Convolutions.pdf [785.6K]
┃ ┣━━Lesson 15.1 学习率调度基本概念与手动实现方法.ipynb [128.2K]
┃ ┣━━Lesson 15.2 学习率调度在PyTorch中的实现方法.ipynb [158.2K]
┃ ┣━━Lesson 16 计算机视觉入门（上）.pdf [8.2M]
┃ ┗━━Lesson 16.1~16.6.ipynb [5.6M]
┣━━01.视频 [67.1G]
┃ ┣━━01.2021PyTorch深度学习实战.abc [523.6M]
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┗━━加密播放器下载地址.txt [105B]

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