李飞飞讲深度学习

deeplearning-master [95.10M]

cs231n [93.35M]

homeworks [10.31M]

assignment1 [2.08M]

.ipynb_checkpoints

cs231n [48.77K]

classifiers [33.05K]

__init__.py [0.10K]

k_nearest_neighbor.py [8.15K]

linear_classifier.py [5.50K]

linear_svm.py [4.59K]

neural_net.py [10.83K]

softmax.py [3.88K]

datasets [0.22K]

.gitignore [0.09K]

get_datasets.sh [0.13K]

__init__.py

data_utils.py [5.42K]

features.py [4.69K]

gradient_check.py [3.48K]

vis_utils.py [1.91K]

.gitignore [0.02K]

collectsubmission.sh [0.16K]

features.ipynb [339.13K]

frameworkpython [0.40K]

knn.ipynb [391.42K]

readme.md [0.13K]

requirements.txt [0.77K]

softmax.ipynb [60.02K]

start_ipython_osx.sh [0.11K]

svm.ipynb [446.08K]

two_layer_net.ipynb [839.54K]

assignment2 [1.30M]

cs231n [96.47K]

classifiers [21.84K]

__init__.py

cnn.py [6.36K]

fc_net.py [15.48K]

datasets [0.22K]

.gitignore [0.09K]

get_datasets.sh [0.13K]

.gitignore [0.04K]

__init__.py

data_utils.py [6.70K]

fast_layers.py [9.07K]

gradient_check.py [3.48K]

im2col.py [2.04K]

im2col_cython.pyx [4.87K]

layer_utils.py [3.06K]

layers.py [27.47K]

optim.py [6.08K]

setup.py [0.29K]

solver.py [9.41K]

vis_utils.py [1.91K]

.gitignore [0.02K]

batchnormalization.ipynb [219.90K]

collectsubmission.sh [0.19K]

convolutionalnetworks.ipynb [372.07K]

dropout.ipynb [51.79K]

frameworkpython [0.40K]

fullyconnectednets.ipynb [523.27K]

kitten.jpg [20.85K]

puppy.jpg [37.49K]

readme.md [5.76K]

requirements.txt [0.78K]

start_ipython_osx.sh [0.11K]

assignment3 [6.94M]

cs231n [97.07K]

classifiers [21.07K]

__init__.py

pretrained_cnn.py [9.62K]

rnn.py [11.45K]

datasets [0.32K]

.gitignore [0.06K]

get_coco_captioning.sh [0.10K]

get_pretrained_model.sh [0.05K]

get_tiny_imagenet_a.sh [0.11K]

__init__.py

captioning_solver.py [8.05K]

coco_utils.py [2.59K]

data_utils.py [7.25K]

fast_layers.py [9.07K]

gradient_check.py [3.48K]

im2col.py [2.04K]

im2col_cython.pyx [4.87K]

image_utils.py [2.29K]

layer_utils.py [4.08K]

layers.py [8.95K]

optim.py [2.68K]

rnn_layers.py [20.05K]

setup.py [0.29K]

.gitignore [0.02K]

collectsubmission.sh [0.19K]

frameworkpython [0.40K]

imagegeneration.ipynb [1.05M]

imagegradients.ipynb [819.23K]

kitten.jpg [20.85K]

lstm_captioning.ipynb [1.17M]

requirements.txt [0.78K]

rnn_captioning.ipynb [3.67M]

sky.jpg [144.99K]

start_ipython_osx.sh [0.11K]

untitled.ipynb [2.38K]

notes [13.68M]

images [13.67M]

l10_image_caption.png [248.84K]

l10_lstm.png [132.07K]

l10_rnn_layer.png [29.47K]

l10_rnn_layer2.png [31.17K]

l10_rnn_layer3.png [75.58K]

l10_summary.png [122.56K]

l11_fft.png [54.21K]

l11_im2col.png [60.33K]

l11_stack_cnn.png [66.57K]

l11_transfer_learning.png [206.65K]

l13_cascades.png [343.87K]

l13_hypercolumns.png [193.85K]

l13_multi_scale.png [352.56K]

l13_refinement.png [471.60K]

l13_semantic_segmentation_cnn.png [745.36K]

l13_similar_to_rcnn.png [374.35K]

l13_soft_attentation_for_caption.png [161.60K]

l13_soft_vs_hard1.png [206.07K]

l13_soft_vs_hard2.png [211.55K]

l13_upsampling.png [278.63K]

l2_deep_learning_pipline.png [291.39K]

l2_traditional_pipeline.png [190.44K]

l3_softmax_function.png [10.58K]

l3_softmax_loss_function.png [12.38K]

l3_svm_loss.png [45.75K]

l3_svm_loss_with_regularization.png [723.39K]

l4_activation_function.png [560.35K]

l4_backpropagation.png [53.99K]

l4_nerual.png [108.52K]

l5_batch_normalization.png [245.08K]

l5_parameters_initialization.png [41.15K]

l6_dropout.png [900.16K]

l7_convolutional_layer.png [418.15K]

l7_pooling_layer.png [209.31K]

l7_summary.png [123.11K]

l8_computer_vision_tasks.png [504.18K]

l8_localization_as_regression.png [164.32K]

l8_overfeat_1.png [133.09K]

l8_overfeat_2.png [191.42K]

l8_recap.png [151.81K]

l8_selective_search.png [366.91K]

l9_deconvolution_approaches.png [141.13K]

l9_deep_dream.png [289.64K]

l9_image_gradient.png [260.84K]

l9_image_reconstructure.png [85.63K]

l9_occlusion_experiments.png [237.09K]

l9_optimization_to_image.png [85.22K]

l9_t_sne.png [183.71K]

l9_visualize_activations.png [127.52K]

l9_visualize_deconvolution.png [1.58M]

l9_visualize_filers.png [353.07K]

l9_visualize_patches.png [801.97K]

l1_introduction.md [0.10K]

l10_recurrent_neural_networks.md [0.67K]

l11_cnns_in_practice.md [0.96K]

l13_segmentation_and_attention.md [2.14K]

l14_videos_and_unspervised_learning.md [0.15K]

l2_image_classification_pipeline.md [0.96K]

l3_loss_functions_and_optimization.md [1.03K]

l4_backpropagation_and_neural_networks.md [1.37K]

l5_training_neural_networks_part_1.md [1.06K]

l6_training_neural_networks_part_2.md [1.68K]

l7_convoluational_neural_networks.md [0.75K]

l8_spatial_localization_and_detection.md [2.63K]

l9_understanding_and_visualizing_cnns.md [4.04K]

slides [69.36M]

stanford university cs231n_ convolutional neural networks for visual recognition.pdf [88.77K]

winter1516_lecture1.pdf [9.45M]

winter1516_lecture10.pdf [7.15M]

winter1516_lecture11.pdf [4.06M]

winter1516_lecture12.pdf [5.94M]

winter1516_lecture13.pdf [5.12M]

winter1516_lecture14.pdf [3.99M]

winter1516_lecture2.pdf [2.53M]

winter1516_lecture3.pdf [2.55M]

winter1516_lecture4.pdf [2.17M]

winter1516_lecture5.pdf [4.30M]

winter1516_lecture6.pdf [5.67M]

winter1516_lecture7.pdf [2.43M]

winter1516_lecture8.pdf [5.39M]

winter1516_lecture9.pdf [8.52M]

deep_learning_with_python [1.48M]

dlwp [1.48M]

data_set [162.16K]

.gitignore [0.07K]

get_housing_data.sh [0.09K]

get_iris_data.sh [0.09K]

get_pima_indians_diabetes_data.sh [0.12K]

get_sonar_data.sh [0.16K]

international-airline-passengers.csv [2.28K]

wonderland.txt [159.36K]

figures [112.49K]

c19_cnn_structure.png [75.44K]

c20_save_augumented_images.png [37.05K]

models [383.14K]

c13 [9.21K]

simple_nn.h5 [8.25K]

simple_nn.json [0.96K]

c14 [373.92K]

nn-00–0.63.h5 [20.77K]

nn-01–0.64.h5 [20.77K]

nn-05–0.75.h5 [20.77K]

nn-10–0.75.h5 [20.77K]

nn-12–0.76.h5 [20.77K]

nn-13–0.76.h5 [20.77K]

nn-19–0.76.h5 [20.77K]

nn-20–0.77.h5 [20.77K]

nn-24–0.77.h5 [20.77K]

nn-27–0.78.h5 [20.77K]

nn-30–0.79.h5 [20.77K]

nn-34–0.79.h5 [20.77K]

nn-42–0.80.h5 [20.77K]

nn-45–0.81.h5 [20.77K]

nn-49–0.81.h5 [20.77K]

nn-51–0.82.h5 [20.77K]

nn-56–0.83.h5 [20.77K]

nn-best-model.h5 [20.77K]

c28 [0.01K]

.gitignore [0.01K]

others [4.60K]

images [4.60K]

aug_0_1119.png [0.56K]

aug_0_7671.png [0.10K]

aug_1_6272.png [0.08K]

aug_1_8474.png [0.47K]

aug_2_1863.png [0.08K]

aug_2_6188.png [0.38K]

aug_3_407.png [0.33K]

aug_3_7264.png [0.08K]

aug_4_6203.png [0.38K]

aug_4_8941.png [0.08K]

aug_5_6914.png [0.08K]

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aug_6_5446.png [0.09K]

aug_6_6409.png [0.46K]

aug_7_547.png [0.09K]

aug_7_6061.png [0.36K]

aug_8_6809.png [0.56K]

aug_8_7553.png [0.35K]

c02_instoduction_to_theano.ipynb [3.79K]

c03_introduction_to_keras.ipynb [6.94K]

c04_introduction_to_tensorflow.ipynb [5.13K]

c07_develop_your_first_neural_network_with_keras.ipynb [25.34K]

c08_evaluate_the_performance_of_model.ipynb [54.40K]

c09_use_keras_models_with_scikit-learn_for_general_machine_learning.ipynb [41.94K]

c10_project_multiclass_classification.ipynb [7.17K]

c11_project_binary_classification_of_sonar_returns.ipynb [9.71K]

c12_project_regression_of_boston_house_price.ipynb [5.75K]

c13_save_and_load_keras_model.ipynb [8.83K]

c14_checkpoint_the_bset_weights_during_training.ipynb [14.52K]

c15_plot_trainging_history_data.ipynb [37.87K]

c16_reduce_overfit_with_dropout.ipynb [3.74K]

c17_lift_performance_with_learning_rate_schedule.ipynb [19.13K]

c19_project_handwritten_digit_recognition.ipynb [28.64K]

c20_image_data_augumentation_with_image_data_generator.ipynb [341.21K]

c21_image_classification_with_cnn.ipynb [121.68K]

c22_project_predict_sentiment_with_movie_review.ipynb [13.64K]

c23_project_predict_time_series_with_fcnn.ipynb [80.86K]

c25_sequence_classification_with_lstm.ipynb [6.41K]

c28_generating_text_with_lstm.ipynb [14.03K]

.gitignore [0.01K]

readme.md [0.91K]

requirements.txt [0.84K]

start_ipython_notebook.sh [0.06K]

keras_practice [231.59K]

kps [229.88K]

figures [80.97K]

multi-input-multi-output-graph.png [80.97K]

fine_tune_vgg16.ipynb [13.82K]

imdb_lstm.ipynb [10.51K]

stateful_lstm.ipynb [115.99K]

untitled.ipynb [8.59K]

.gitignore [0.01K]

readme.md [0.81K]

requirements.txt [0.84K]

start_ipython_notebook.sh [0.04K]

others [42.59K]

requirements.txt [0.10K]

start_notebook.sh [0.06K]

visualize_high_dimensional_data.ipynb [42.43K]

.gitignore [1.05K]

readme.md [0.37K]

1.计算机视觉历史回顾与介绍上.mp4 [206.27M]

10.神经网络训练细节part1（上）.mp4 [300.84M]

11.神经网络训练细节part1（下）.mp4 [301.21M]

12.神经网络训练细节part2（上）.mp4 [268.28M]

13.神经网络训练细节part2（下）.mp4 [263.18M]

14.卷积神经网络详解（上）.mp4 [299.44M]

15.卷积神经网络详解（下）.mp4 [300.03M]

16.迁移学习之物体定位于检测（上）.mp4 [278.07M]

17.迁移学习之物体定位于检测（下）.mp4 [218.63M]

18.卷积神经网络的可视化与进一步理解（上）.mp4 [297.65M]

19.卷积神经网络的可视化与进一步理解（下）.mp4 [299.59M]

2.计算机视觉历史回顾与介绍中.mp4 [162.32M]

20.循环神经网络（上）.mp4 [228.60M]

21.循环神经网络（下）.mp4 [298.38M]

22.卷积神经网络工程实践技巧与注意点（上）.mp4.mp4 [274.16M]

23.卷积神经网络工程实践技巧与注意点（下）.mp4 [293.29M]

24.深度学习开源库使用介绍（上）.mp4.mp4 [320.04M]

25.深度学习开源库使用介绍（下）.mp4.mp4 [298.87M]

26.图像分割与注意力模型（上）.mp4.mp4 [254.15M]

27.图像分割与注意力模型（下）.mp4.mp4 [286.64M]

28.视频检测与无监督学习（上）.mp4.mp4 [273.94M]

29.视频检测与无监督学习（下）.mp4.mp4 [325.21M]

3.计算机视觉历史回顾与介绍下.mp4 [198.28M]

30.来自jeff dean的受邀报告（上）.mp4.mp4 [276.90M]

31.来自jeff dean的受邀报告（下）.mp4 [292.70M]

4.数据驱动的图像分类方式：k最邻近与线性分类器（上）.mp4 [222.04M]

5.数据驱动的图像分类方式：k最邻近与线性分类器（下）.mp4 [217.65M]

6.线性分类器损失函数与最优化（上）.mp4 [274.00M]

7.线性分类器损失函数与最优化（下）.mp4 [269.00M]

8.反向传播与神经网络初步（上）.mp4 [307.88M]

9.反向传播与神经网络初步（下）.mp4 [306.58M]