深度学习框架PyTorch：入门与实践 - 陈云_1649413378599.pdf
深度学习入门之PyTorch - 廖星宇（有目录）_1649413376552.pdf
深度学习之Pytorch - 廖星宇_1649413373579.pdf
深度学习之PyTorch实战计算机视觉 - 唐进民_1649413280487.pdf
pytorch卷积、反卷积 - download from internet_1649413279000.pdf
pytorch 0.4 - tutorial - 有目录版_1649413278219.pdf
pytorch-internals_1649413278721.pdf
PyTorch_tutorial_0.0.4_余霆嵩_1649413276768.pdf
PyTorch_tutorial_0.0.5_余霆嵩_1649413277437.pdf
PyTorch深度学习实战 - 侯宜军_1649413277689.pdf
해커톤 문제정의_1649413275562.pptx
template_1649413274944.pptx
Lecture 14_ Seq2Seq_1649413274105.pptx
Lecture 15_ NSML, Smartest ML Platform_1649413274515.pptx
P-Epilogue_ What_s the next__1649413274673.pptx
Lecture 12_ RNN_1649413271722.pptx
Lecture 13_ RNN II_1649413273622.pptx
Lecture 11_ Advanced CNN_1649413271253.pptx
Lecture 10_ Basic CNN_1649413270147.pptx
Lecture 07_ Wide _ Deep_1649413268991.pptx
Lecture 08_ DataLoader_1649413269126.pptx
Lecture 09_ Softmax Classifier_1649413269418.pptx
Lecture 02_ Linear Model_1649413267973.pptx
Lecture 03_ Gradient Descent_1649413268076.pptx
Lecture 04_ Back-propagation and PyTorch autograd_1649413268287.pptx
Lecture 05_ Linear regression in PyTorch way_1649413268580.pptx
Lecture 06_ Logistic Regression_1649413268791.pptx
Lecture 01_ Overview_1649413266841.pptx
PyTorch under the hood A guide to understand PyTorch internals_1649413266053.pdf
PyTorch Recipes - A Problem-Solution Approach - Pradeepta Mishra_1649413265373.pdf
Introduction to Tensorflow, PyTorch and Caffe_1649413263711.pdf
PyTorch 0.4 中文文档 - 翻译_1649413264237.pdf
PyTorch 1.0 Bringing research and production together Presentation_1649413264536.pdf
Deep_Learning_with_PyTorch_Quick_Start_Guide_1649413263110.pdf
First steps towards deep learning with pytorch_1649413263409.pdf
Deep-Learning-with-PyTorch_1649413259088.pdf
ee559-slides-9-2-autoencoders_1649413258047.pdf
ee559-slides-9-3-denoising-and-variational-autoencoders_1649413258088.pdf
ee559-slides-9-4-NVP_1649413258474.pdf
ee559-slides-8-2-looking-at-activations_1649413256925.pdf
ee559-slides-8-3-visualizing-in-input_1649413257228.pdf
ee559-slides-8-4-optimizing-inputs_1649413257662.pdf
ee559-slides-9-1-transposed-convolutions_1649413257831.pdf
ee559-slides-7-1-CV-tasks_1649413255999.pdf
ee559-slides-7-2-image-classification_1649413256090.pdf
ee559-slides-7-3-object-detection_1649413256334.pdf
ee559-slides-7-4-segmentation_1649413256464.pdf
ee559-slides-7-5-dataloader-and-surgery_1649413256682.pdf
ee559-slides-8-1-looking-at-parameters_1649413256743.pdf
ee559-slides-5-4-l2-l1-penalties_1649413254949.pdf
ee559-slides-5-5-initialization_1649413255113.pdf
ee559-slides-5-6-architecture-and-training_1649413255250.pdf
ee559-slides-5-7-writing-an-autograd-function_1649413255384.pdf
ee559-slides-6-1-benefits-of-depth_1649413255450.pdf
ee559-slides-6-2-rectifiers_1649413255506.pdf
ee559-slides-6-3-dropout_1649413255585.pdf
ee559-slides-6-4-batch-normalization_1649413255640.pdf
ee559-slides-6-5-residual-networks_1649413255722.pdf
ee559-slides-6-6-using-GPUs_1649413255768.pdf
ee559-slides-4-1-DAG-networks_1649413253994.pdf
ee559-slides-4-2-autograd_1649413254076.pdf
ee559-slides-4-3-modules-and-batch-processing_1649413254207.pdf
ee559-slides-4-4-convolutions_1649413254274.pdf
ee559-slides-4-5-pooling_1649413254355.pdf
ee559-slides-4-6-writing-a-module_1649413254453.pdf
ee559-slides-5-1-cross-entropy-loss_1649413254622.pdf
ee559-slides-5-2-SGD_1649413254742.pdf
ee559-slides-5-3-optim_1649413254831.pdf
ee559-slides-2-5-basic-embeddings_1649413253017.pdf
ee559-slides-3-1-perceptron_1649413253087.pdf
ee559-slides-3-2-LDA_1649413253210.pdf
ee559-slides-3-3-features_1649413253708.pdf
ee559-slides-3-4-MLP_1649413253796.pdf
ee559-slides-3-5-gradient-descent_1649413253891.pdf
ee559-slides-3-6-backprop_1649413253946.pdf
ee559-slides-10-3-conditional-GAN_1649413252134.pdf
ee559-slides-10-4-persistence_1649413252279.pdf
ee559-slides-11-1-RNN-basics_1649413252407.pdf
ee559-slides-11-2-LSTM-and-GRU_1649413252443.pdf
ee559-slides-11-3-word-embeddings-and-translation_1649413252513.pdf
ee559-slides-2-1-loss-and-risk_1649413252569.pdf
ee559-slides-2-2-overfitting_1649413252632.pdf
ee559-slides-2-3-bias-variance-dilemma_1649413252687.pdf
ee559-slides-2-4-evaluation-protocols_1649413252803.pdf
ee559-slides-1-6-tensor-internals_1649413250998.pdf
ee559-slides-10-1-GAN_1649413251292.pdf
ee559-slides-10-2-Wasserstein-GAN_1649413251720.pdf
ee559-slides-1-2-current-success_1649413250162.pdf
ee559-slides-1-3-what-is-happening_1649413250547.pdf
ee559-slides-1-4-tensors-and-linear-regression_1649413250809.pdf
ee559-slides-1-5-high-dimension-tensors_1649413250932.pdf
ee559-handout-8-4-optimizing-inputs_1649413248850.pdf
ee559-handout-9-1-transposed-convolutions_1649413249283.pdf
ee559-handout-9-2-autoencoders_1649413249349.pdf
ee559-handout-9-3-denoising-and-variational-autoencoders_1649413249491.pdf
ee559-handout-9-4-NVP_1649413249618.pdf
ee559-slides-1-1-from-anns-to-deep-learning_1649413249776.pdf
ee559-handout-8-1-looking-at-parameters_1649413248113.pdf
ee559-handout-8-2-looking-at-activations_1649413248297.pdf
ee559-handout-8-3-visualizing-in-input_1649413248600.pdf