#9527

【深度之眼】李飞飞讲深度学习视频课程

课程介绍

课程来自于 【深度之眼】李飞飞讲深度学习

课程大纲

DeepLearning-master

others

visualize_high_dimensional_data.ipynb

start_notebook.sh

requirements.txt

keras_practice

kps

figures

multi-input-multi-output-graph.png

Untitled.ipynb

stateful_lstm.ipynb

imdb_lstm.ipynb

fine_tune_vgg16.ipynb

start_ipython_notebook.sh

requirements.txt

readme.md

.gitignore

deep_learning_with_python

dlwp

others

images

aug_8_7553.png

aug_8_6809.png

aug_7_6061.png

aug_7_547.png

aug_6_6409.png

aug_6_5446.png

aug_5_7587.png

aug_5_6914.png

aug_4_8941.png

aug_4_6203.png

aug_3_7264.png

aug_3_407.png

aug_2_6188.png

aug_2_1863.png

aug_1_8474.png

aug_1_6272.png

aug_0_7671.png

aug_0_1119.png

models

c28

.gitignore

c14

nn-best-model.h5

nn-56--0.83.h5

nn-51--0.82.h5

nn-49--0.81.h5

nn-45--0.81.h5

nn-42--0.80.h5

nn-34--0.79.h5

nn-30--0.79.h5

nn-27--0.78.h5

nn-24--0.77.h5

nn-20--0.77.h5

nn-19--0.76.h5

nn-13--0.76.h5

nn-12--0.76.h5

nn-10--0.75.h5

nn-05--0.75.h5

nn-01--0.64.h5

nn-00--0.63.h5

c13

simple_nn.json

simple_nn.h5

figures

c20_save_augumented_images.png

c19_cnn_structure.png

data_set

wonderland.txt

international-airline-passengers.csv

get_sonar_data.sh

get_pima_indians_diabetes_data.sh

get_iris_data.sh

get_housing_data.sh

.gitignore

c28_generating_text_with_lstm.ipynb

c25_sequence_classification_with_lstm.ipynb

c23_project_predict_time_series_with_fcnn.ipynb

c22_project_predict_sentiment_with_movie_review.ipynb

c21_image_classification_with_cnn.ipynb

c20_image_data_augumentation_with_image_data_generator.ipynb

c19_project_handwritten_digit_recognition.ipynb

c17_lift_performance_with_learning_rate_schedule.ipynb

c16_reduce_overfit_with_dropout.ipynb

c15_plot_trainging_history_data.ipynb

c14_checkpoint_the_bset_weights_during_training.ipynb

c13_save_and_load_keras_model.ipynb

c12_project_regression_of_boston_house_price.ipynb

c11_project_binary_classification_of_sonar_returns.ipynb

c10_project_multiclass_classification.ipynb

c09_use_keras_models_with_scikit-learn_for_general_machine_learning.ipynb

c08_evaluate_the_performance_of_model.ipynb

c07_develop_your_first_neural_network_with_keras.ipynb

c04_introduction_to_tensorflow.ipynb

c03_introduction_to_keras.ipynb

c02_instoduction_to_theano.ipynb

start_ipython_notebook.sh

requirements.txt

readme.md

.gitignore

cs231n

Slides

winter1516_lecture9.pdf

winter1516_lecture8.pdf

winter1516_lecture7.pdf

winter1516_lecture6.pdf

winter1516_lecture5.pdf

winter1516_lecture4.pdf

winter1516_lecture3.pdf

winter1516_lecture2.pdf

winter1516_lecture14.pdf

winter1516_lecture13.pdf

winter1516_lecture12.pdf

winter1516_lecture11.pdf

winter1516_lecture10.pdf

winter1516_lecture1.pdf

Stanford University CS231n_ Convolutional Neural Networks for Visual Recognition.pdf

Notes

Images

l9_visualize_patches.png

l9_visualize_filers.png

l9_visualize_deconvolution.png

l9_visualize_activations.png

l9_t_sne.png

l9_optimization_to_image.png

l9_occlusion_experiments.png

l9_image_reconstructure.png

l9_image_gradient.png

l9_deep_dream.png

l9_deconvolution_approaches.png

l8_selective_search.png

l8_recap.png

l8_overfeat_2.png

l8_overfeat_1.png

l8_localization_as_regression.png

l8_computer_vision_tasks.png

l7_summary.png

l7_pooling_layer.png

l7_convolutional_layer.png

l6_dropout.png

l5_parameters_initialization.png

l5_batch_normalization.png

l4_nerual.png

l4_backpropagation.png

l4_activation_function.png

l3_svm_loss_with_regularization.png

l3_svm_loss.png

l3_softmax_loss_function.png

l3_softmax_function.png

l2_traditional_pipeline.png

l2_deep_learning_pipline.png

l13_upsampling.png

l13_soft_vs_hard2.png

l13_soft_vs_hard1.png

l13_soft_attentation_for_caption.png

l13_similar_to_rcnn.png

l13_semantic_segmentation_cnn.png

l13_refinement.png

l13_multi_scale.png

l13_hypercolumns.png

l13_cascades.png

l11_transfer_learning.png

l11_stack_cnn.png

l11_im2col.png

l11_fft.png

l10_summary.png

l10_rnn_layer3.png

l10_rnn_layer2.png

l10_rnn_layer.png

l10_lstm.png

l10_image_caption.png

L9_Understanding_and_Visualizing_CNNs.md

L8_Spatial_Localization_and_Detection.md

L7_Convoluational_Neural_Networks.md

L6_Training_Neural_Networks_part_2.md

L5_Training_Neural_Networks_part_1.md

L4_Backpropagation_and_Neural_Networks.md

L3_Loss_Functions_and_Optimization.md

L2_Image_Classification_Pipeline.md

L1_Introduction.md

L14_Videos_and_Unspervised_Learning.md

L13_Segmentation_and_Attention.md

L11_CNNs_in_practice.md

L10_Recurrent_Neural_Networks.md

HomeWorks

assignment3

cs231n

datasets

get_tiny_imagenet_a.sh

get_pretrained_model.sh

get_coco_captioning.sh

.gitignore

classifiers

__init__.py

rnn.py

pretrained_cnn.py

__init__.py

setup.py

rnn_layers.py

optim.py

layer_utils.py

layers.py

image_utils.py

im2col_cython.pyx

im2col.py

gradient_check.py

fast_layers.py

data_utils.py

coco_utils.py

captioning_solver.py

Untitled.ipynb

start_ipython_osx.sh

sky.jpg

RNN_Captioning.ipynb

requirements.txt

LSTM_Captioning.ipynb

kitten.jpg

ImageGradients.ipynb

ImageGeneration.ipynb

frameworkpython

collectSubmission.sh

.gitignore

assignment2

cs231n

datasets

get_datasets.sh

.gitignore

classifiers

__init__.py

fc_net.py

cnn.py

__init__.py

vis_utils.py

solver.py

setup.py

optim.py

layer_utils.py

layers.py

im2col_cython.pyx

im2col.py

gradient_check.py

fast_layers.py

data_utils.py

.gitignore

start_ipython_osx.sh

requirements.txt

README.md

puppy.jpg

kitten.jpg

FullyConnectedNets.ipynb

frameworkpython

Dropout.ipynb

ConvolutionalNetworks.ipynb

collectSubmission.sh

BatchNormalization.ipynb

.gitignore

assignment1

cs231n

datasets

get_datasets.sh

.gitignore

classifiers

__init__.py

softmax.py

neural_net.py

linear_svm.py

linear_classifier.py

k_nearest_neighbor.py

__init__.py

vis_utils.py

gradient_check.py

features.py

data_utils.py

.ipynb_checkpoints

two_layer_net.ipynb

svm.ipynb

start_ipython_osx.sh

softmax.ipynb

requirements.txt

README.md

knn.ipynb

frameworkpython

features.ipynb

collectSubmission.sh

.gitignore

README.md

.gitignore

31.来自Jeff Dean的受邀报告（下）.mp4

30.来自Jeff Dean的受邀报告（上）.mp4.mp4

29.视频检测与无监督学习（下）.mp4.mp4

28.视频检测与无监督学习（上）.mp4.mp4

27.图像分割与注意力模型（下）.mp4.mp4

26.图像分割与注意力模型（上）.mp4.mp4

25.深度学习开源库使用介绍（下）.mp4.mp4

24.深度学习开源库使用介绍（上）.mp4.mp4

23.卷积神经网络工程实践技巧与注意点（下）.mp4

22.卷积神经网络工程实践技巧与注意点（上）.mp4.mp4

21.循环神经网络（下）.mp4

20.循环神经网络（上）.mp4

19.卷积神经网络的可视化与进一步理解（下）.mp4

18.卷积神经网络的可视化与进一步理解（上）.mp4

17.迁移学习之物体定位于检测（下）.mp4

16.迁移学习之物体定位于检测（上）.mp4

15.卷积神经网络详解（下）.mp4

14.卷积神经网络详解（上）.mp4

13.神经网络训练细节part2（下）.mp4

12.神经网络训练细节part2（上）.mp4

11.神经网络训练细节part1（下）.mp4

10.神经网络训练细节part1（上）.mp4

9.反向传播与神经网络初步（下）.mp4

8.反向传播与神经网络初步（上）.mp4

7.线性分类器损失函数与最优化（下）.mp4

6.线性分类器损失函数与最优化（上）.mp4

5.数据驱动的图像分类方式：k最邻近与线性分类器（下）.mp4

4.数据驱动的图像分类方式：k最邻近与线性分类器（上）.mp4

3.计算机视觉历史回顾与介绍下.mp4

2.计算机视觉历史回顾与介绍中.mp4

1.计算机视觉历史回顾与介绍上.mp4