李宏毅机器学习课程最新更新2021

本课程为李宏毅机器学习课程，更新最新2021年。包含视频和代码。

学习作业图：http://speech.ee.ntu.edu.tw/~tlkagk/courses_ML20.html

2020年课程

├──code 

|   ├──hw1 

|   |   ├──hw1_regression.ipynb  13.55kb

|   |   ├──hw1_regression.py  7.44kb

|   |   └──hw1_slides.pptx  1.14M

|   ├──hw10 

|   |   ├──hw10_anomaly_detection.ipynb  18.14kb

|   |   ├──hw10_anomaly_detection.py  10.30kb

|   |   └──hw10_slides.pptx  849.67kb

|   ├──hw11 

|   |   ├──hw11_GAN.ipynb  450.75kb

|   |   ├──hw11_gan.py  208.96kb

|   |   └──hw11_slides.pptx  4.93M

|   ├──hw12 

|   |   ├──hw12_domain_adaptation.ipynb  129.83kb

|   |   ├──hw12_domain_adaptation.py  14.79kb

|   |   └──hw12_slides.pptx  1.01M

|   ├──hw13 

|   |   ├──hw13_2.pptx  1.62M

|   |   ├──hw13_meta_omniglot.ipynb  198.49kb

|   |   ├──hw13_meta_omniglot.py  143.83kb

|   |   ├──hw13_meta_regression.ipynb  89.05kb

|   |   ├──hw13_meta_regression.py  11.71kb

|   |   └──hw13_slides.pptx  730.35kb

|   ├──hw14 

|   |   ├──hw14_life_long_learning.ipynb  49.75kb

|   |   ├──hw14_life_long_learning.py  31.05kb

|   |   └──hw14_slides.pptx  648.83kb

|   ├──hw15 

|   |   ├──hw15_reinforcement_learning.ipynb  111.64kb

|   |   ├──hw15_reinforcement_learning.py  10.50kb

|   |   └──hw15_slides.pptx  1.04M

|   ├──hw2 

|   |   ├──hw2_classification.ipynb  67.12kb

|   |   ├──hw2_classification.py  16.49kb

|   |   └──hw2_slides.pptx  483.96kb

|   ├──hw3 

|   |   ├──hw3_CNN.ipynb  15.64kb

|   |   ├──hw3_cnn.py  8.83kb

|   |   └──hw3_slides.pptx  750.81kb

|   ├──hw4 

|   |   ├──hw4_RNN.ipynb  60.12kb

|   |   ├──hw4_rnn.py  18.16kb

|   |   └──hw4_slides.pptx  2.48M

|   ├──hw5 

|   |   ├──hw5_colab.ipynb  1.03M

|   |   ├──hw5_colab.py  18.89kb

|   |   └──hw5_slide.pptx  11.24M

|   ├──hw6 

|   |   ├──hw6_adversarial_attack.ipynb  13.21kb

|   |   ├──hw6_adversarial_attack.py  7.27kb

|   |   └──hw6_slides.pptx  906.59kb

|   ├──hw7 

|   |   ├──hw7_Architecture_Design.ipynb  10.56kb

|   |   ├──hw7_architecture_design.py  7.38kb

|   |   ├──hw7_Knowledge_Distillation.ipynb  26.70kb

|   |   ├──hw7_knowledge_distillation.py  8.71kb

|   |   ├──hw7_Network_Pruning.ipynb  342.51kb

|   |   ├──hw7_network_pruning.py  11.01kb

|   |   ├──hw7_slides.pptx  1.73M

|   |   ├──hw7_Weight_Quantization.ipynb  9.80kb

|   |   └──hw7_weight_quantization.py  5.06kb

|   ├──hw8 

|   |   ├──hw8_seq2seq.ipynb  43.71kb

|   |   ├──hw8_seq2seq.py  25.19kb

|   |   └──hw8_slides.pptx  1.29M

|   └──hw9 

|   |   ├──hw9_slides.pptx  4.78M

|   |   ├──hw9_unsupervised.ipynb  205.01kb

|   |   └──hw9_unsupervised.py  11.44kb

├──datasets 

|   ├──hw1 

|   |   └──data.zip  173.29kb

|   ├──hw10 

|   |   ├──test.npy  234.38M

|   |   └──train.npy  937.50M

|   ├──hw11 

|   |   └──crypko_data.zip  431.13M

|   ├──hw12 

|   |   └──real_or_drawing.zip  183.60M

|   ├──hw13 

|   |   └──Omniglot.tar.gz  34.38M

|   ├──hw2 

|   |   └──data.tar.gz  5.82M

|   ├──hw3 

|   |   └──food-11.zip  1.08G

|   ├──hw4 

|   |   └──data.zip  43.01M

|   ├──hw5 

|   |   ├──checkpoint.pth  162.13M

|   |   └──food-11同hw3.txt 

|   ├──hw6 

|   |   └──data.zip  17.06M

|   ├──hw7 

|   |   ├──food-11同hw3.txt 

|   |   └──student_custom_small.bin  1022.88kb

|   ├──hw8 

|   |   └──data.tar.gz  5.56M

|   └──hw9 

|   |   ├──trainX.npy  24.90M

|   |   ├──valX.npy  1.46M

|   |   └──valY.npy  4.03kb

├──slides 

|   ├──AC.pdf  798.13kb

|   ├──Attack (v8).pdf  2.55M

|   ├──Attain (v5).pdf  3.39M

|   ├──Auto (v3).pdf  1.62M

|   ├──auto.pdf  1.82M

|   ├──Bias and Variance (v2).pdf  1.04M

|   ├──BP.pdf  914.31kb

|   ├──CGAN.pdf  934.44kb

|   ├──Classification (v3).pdf  1.37M

|   ├──CNN.pdf  3.46M

|   ├──CycleGAN.pdf  2.03M

|   ├──Detection (v9).pdf  2.21M

|   ├──DL (v2).pdf  1.74M

|   ├──DNN tip.pdf  2.38M

|   ├──fGAN.pdf  516.87kb

|   ├──GAN (v2).pdf  2.95M

|   ├──GANEvaluation.pdf  559.20kb

|   ├──GANfeature.pdf  978.68kb

|   ├──GANSeqNew.pdf  2.04M

|   ├──GANtheory (v2).pdf  706.17kb

|   ├──GNN.pdf  30.62M

|   ├──Gradient Descent (v2).pdf  1.53M

|   ├──introduction.pdf  1.95M

|   ├──IRL (v2).pdf  844.74kb

|   ├──Lifelong Learning (v9).pdf  1.71M

|   ├──Logistic Regression (v3).pdf  1.55M

|   ├──Meta1 (v6).pdf  1.61M

|   ├──Meta2 (v4).pdf  1.82M

|   ├──PCA (v3).pdf  1.77M

|   ├──PhotoEditing.pdf  1.19M

|   ├──Pointer.pdf  738.98kb

|   ├──PPO (v3).pdf  874.02kb

|   ├──QLearning (v2).pdf  1.60M

|   ├──Recursive.pdf  872.47kb

|   ├──Regression.pdf  1.57M

|   ├──Reward (v3).pdf  856.92kb

|   ├──RL (v6).pdf  2.29M

|   ├──RNN (v2).pdf  3.79M

|   ├──rule.pdf  635.41kb

|   ├──semi (v3).pdf  1.34M

|   ├──Small (v6).pdf  1.78M

|   ├──transfer (v3).pdf  2.44M

|   ├──TSNE.pdf  1.09M

|   ├──WGAN (v2).pdf  1.01M

|   ├──Why.pdf  2.77M

|   ├──word2vec (v2).pdf  1.39M

|   └──XAI (v7).pdf  3.36M

├──video 

|   ├──course 

|   |   ├──P10_DL預備_ML_Lecture_6_Brief_Introduction_of_Deep_Learning.flv  91.13M

|   |   ├──P11_ML_Lecture_7_Backpropagation.flv  64.09M

|   |   ├──P12_ML_Lecture_9-1_Tips_for_Training_DNN.flv  171.80M

|   |   ├──P13_ML_Lecture_9-2_Keras_Demo_2.flv  54.34M

|   |   ├──P14_ML_Lecture_9-3_Fizz_Buzz_in_Tensorflow_sequel.flv  12.64M

|   |   ├──P15_作業三_ML_Lecture_10_Convolutional_Neural_Network.flv  153.30M

|   |   ├──P16_作業四_ML_Lecture_21-1_Recurrent_Neural_Network_Part_I.flv  95.06M

|   |   ├──P17_ML_Lecture_21-2_Recurrent_Neural_Network_Part_II.flv  186.45M

|   |   ├──P18_作業五_Explainable_ML_1_8.flv  25.87M

|   |   ├──P19_Explainable_ML_2_8.flv  27.73M

|   |   ├──P1_Course_Introduction.flv  77.83M

|   |   ├──P20_Explainable_ML_3_8.flv  11.58M

|   |   ├──P21_Explainable_ML_4_8.flv  13.75M

|   |   ├──P22_Explainable_ML_5_8.flv  15.49M

|   |   ├──P23_Explainable_ML_6_8.flv  14.09M

|   |   ├──P24_Explainable_ML_7_8.flv  15.40M

|   |   ├──P25_Explainable_ML_8_8.flv  13.76M

|   |   ├──P26_作業六_Attack_ML_Models_1_8.flv  11.64M

|   |   ├──P27_Attack_ML_Models_2_8.flv  20.60M

|   |   ├──P28_Attack_ML_Models_3_8.flv  13.62M

|   |   ├──P29_Attack_ML_Models_4_8.flv  15.55M

|   |   ├──P2_Rule_of_ML_2020.flv  46.40M

|   |   ├──P30_Attack_ML_Models_5_8.flv  14.94M

|   |   ├──P31_Attack_ML_Models_6_8.flv  17.86M

|   |   ├──P32_Attack_ML_Models_7_8.flv  15.11M

|   |   ├──P33_Attack_ML_Models_8_8.flv  19.37M

|   |   ├──P34_作業七_Network_Compression_1_6.flv  14.20M

|   |   ├──P35_Network_Compression_2_6.flv  24.67M

|   |   ├──P36_Network_Compression_3_6.flv  14.17M

|   |   ├──P37_Network_Compression_4_6.flv  12.56M

|   |   ├──P38_Network_Compression_5_6.flv  17.87M

|   |   ├──P39_Network_Compression_6_6.flv  23.05M

|   |   ├──P3_作業一_ML_Lecture_1_Regression_Case_Study.flv  148.16M

|   |   ├──P40_作業八_Conditional_Generation_by_RNN___Attention.flv  202.61M

|   |   ├──P41_作業九_ML_Lecture_13_Unsupervised_Learning_Linear_Methods.flv  197.19M

|   |   ├──P42_ML_Lecture_15_Unsupervised_Learning_Neighbor_Embedding.flv  61.07M

|   |   ├──P43_ML_Lecture_16_Unsupervised_Learning_Auto-encoder.flv  81.53M

|   |   ├──P44_作業十_Anomaly_Detection_1_7.flv  25.12M

|   |   ├──P45_Anomaly_Detection_2_7.flv  28.18M

|   |   ├──P46_Anomaly_Detection_3_7.flv  27.08M

|   |   ├──P47_Anomaly_Detection_4_7.flv  7.73M

|   |   ├──P48_Anomaly_Detection_5_7.flv  24.71M

|   |   ├──P49_Anomaly_Detection_6_7.flv  25.46M

|   |   ├──P4_ML_Lecture_2_Where_does_the_error_come_from.flv  85.92M

|   |   ├──P50_Anomaly_Detection_7_7.flv  11.44M

|   |   ├──P51_作業十一_GAN_Lecture_1_2018_Introduction.flv  180.61M

|   |   ├──P52_GAN_Lecture_2_2018_Conditional_Generation.flv  57.04M

|   |   ├──P53_GAN_Lecture_3_2018_Unsupervised_Conditional_Generation.flv  76.79M

|   |   ├──P54_GAN_Lecture_4_2018_Basic_Theory.flv  168.58M

|   |   ├──P55_GAN_Lecture_5_2018_General_Framework.flv  47.51M

|   |   ├──P56_GAN_Lecture_6_2018_WGAN,_EBGAN.flv  86.20M

|   |   ├──P57_GAN_Lecture_7_2018_Info_GAN,_VAE-GAN,_BiGAN.flv  86.20M

|   |   ├──P58_GAN_Lecture_8_2018_Photo_Editing.flv  43.62M

|   |   ├──P59_GAN_Lecture_9_2018_Sequence_Generation.flv  159.57M

|   |   ├──P5_Gradient_Descent_ML_Lecture_3-1_Gradient_Descent.flv  118.33M

|   |   ├──P60_GAN_Lecture_10_2018_Evaluation_Concluding_Remarks.flv  58.19M

|   |   ├──P61_作業十二_ML_Lecture_12_Semi-supervised.flv  116.92M

|   |   ├──P62_ML_Lecture_19_Transfer_Learning.mp4  225.69M

|   |   ├──P63_作業十三Introduction_of_Meta_Learning.flv  24.58M

|   |   ├──P64_作業十四_Life_Long_Learning_1_7.flv  24.58M

|   |   ├──P65_Life_Long_Learning_2_7.flv  14.08M

|   |   ├──P66_Life_Long_Learning_3_7.flv  20.58M

|   |   ├──P67_Life_Long_Learning_4_7.flv  8.77M

|   |   ├──P68_Life_Long_Learning_5_7.flv  6.22M

|   |   ├──P69_Life_Long_Learning_6_7.flv  26.75M

|   |   ├──P6_ML_Lecture_3-2_Gradient_Descent_Demo_by_AOE.flv  9.39M

|   |   ├──P70_Life_Long_Learning_7_7.flv  19.80M

|   |   ├──P71_作業十五_ML_Lecture_23-1_Deep_Reinforcement_Learning.flv  131.58M

|   |   ├──P72_ML_Lecture_23-2_Policy_Gradient_Supplementary_Explanation.flv  25.39M

|   |   ├──P73_ML_Lecture_23-3_Reinforcement_Learning_including_Q-learning.flv  440.70M

|   |   ├──P74_DRL_Lecture_1_Policy_Gradient_Review.flv  82.27M

|   |   ├──P75_DRL_Lecture_2__Proximal_Policy_Optimization_PPO.flv  77.63M

|   |   ├──P76_DRL_Lecture_3_Q-learning_Basic_Idea.flv  88.14M

|   |   ├──P77_DRL_Lecture_4_Q-learning_Advanced_Tips.flv  70.52M

|   |   ├──P78_DRL_Lecture_5_Q-learning_Continuous_Action.flv  28.78M

|   |   ├──P79_DRL_Lecture_6_Actor-Critic.flv  60.69M

|   |   ├──P7_ML_Lecture_3-3_Gradient_Descent_Demo_by_Minecraft.flv  17.46M

|   |   ├──P80_DRL_Lecture_7_Sparse_Reward.flv  58.70M

|   |   ├──P81_DRL_Lecture_8_Imitation_Learning.flv  60.02M

|   |   ├──P8_作業二_ML_Lecture_4_Classification.flv  135.81M

|   |   └──P9_ML_Lecture_5_Logistic_Regression.flv  127.76M

|   ├──extra 

|   |   ├──graph_neual_network_1.flv  85.75M

|   |   └──graph_neural_ network_2.mp4  103.97M

|   ├──homework 

|   |   ├──HW10_Anomaly_Detection.flv  25.77M

|   |   ├──HW11_GAN.flv  23.21M

|   |   ├──HW12_Transfer_Learning.flv  13.20M

|   |   ├──HW13_Meta_Learning_1.flv  34.70M

|   |   ├──HW13_Meta_Learning_2.flv  15.68M

|   |   ├──HW13_Meta_Learning_3.flv  86.97M

|   |   ├──HW14_Life-long_Learning.flv  41.39M

|   |   ├──HW15_Reinforcement_Learning.flv  15.50M

|   |   ├──HW1_Regression.flv  11.75M

|   |   ├──HW2_Classification.flv  27.30M

|   |   ├──HW3_CNN.flv  31.57M

|   |   ├──HW4_RNN.flv  25.56M

|   |   ├──HW5_Explainable.flv  60.88M

|   |   ├──HW6_Adversarial_Attack.flv  18.08M

|   |   ├──HW7_Network_Compression.flv  28.55M

|   |   ├──HW8_Seq2seq.flv  34.68M

|   |   └──HW9_Unsupervised_Learning.flv  17.20M

|   └──names.txt  3.10kb

├──courses_ML20.html  19.71kb

├──HW.png  141.89kb

└──main_ihwang.css  2.16kb

李宏毅2021春机器学习课程/

├──01_2021_机器学习相关规定.mp4  53.29M

├──02_第一节__上____机器学习基本概念简介.mp4  78.49M

├──03__下____深度学习基本概念简介.mp4  84.86M

├──04_Google_Colab教学.mp4  19.48M

├──05_Pytorch_教学_part_1.mp4  42.60M

├──06_Pytorch_教学_part_2_英文有字幕_.mp4  18.57M

├──07_作业说明_HW1_slides.mp4  36.71M

├──08__选修_To_Learn_More___深度学习简介.mp4  77.79M

├──09__选修_To_Learn_More___反向传播_Backpropagation_.mp4  43.62M

├──100__选修_To_Learn_More___Meta_Learning___Metric_based__2_.mp4  4.57M

├──101__选修_To_Learn_More___Meta_Learning___Metric_based__3_.mp4  11.48M

├──102__选修_To_Learn_More___Meta_Learning___Train_Test_as_RNN.mp4  7.63M

├──10_第二节_机器学习任务攻略.mp4  79.99M

├──11_类神经网络训练不起来怎么办_一__局部最小值__local_minima__与鞍点__saddle_point_.mp4  59.09M

├──12_类神经网络训练不起来怎么办_二__批次__batch__与动量__momentum_.mp4  54.53M

├──13_类神经网络训练不起来怎么办_三__自动调整学习率__Learning_Rate_.mp4  60.20M

├──14_类神经网络训练不起来怎么办_四__损失函数__Loss__也可能有影响.mp4  28.81M

├──15_类神经网络训练不起来怎么办__五__批次标准化__Batch_Normalization_.mp4  45.64M

├──16__选修_To_Learn_More___Optimization_for_Deep_Learning__1_2_.mp4  91.83M

├──17__选修_To_Learn_More___Optimization_for_Deep_Learning__2_2_.mp4  94.68M

├──18__选修_To_Learn_More____Classification.mp4  114.47M

├──19__选修_To_Learn_More___Logistic_Regression.mp4  110.19M

├──20_作业说明_HW2中文低画质版.mp4  64.63M

├──21_作业说明_HW2_英文有字幕高清版.mp4  58.98M

├──22_第三节_卷积神经网络_CNN_.mp4  91.24M

├──23_自注意力机制_Self_attention__上_.mp4  42.00M

├──24_自注意力机制__Self_attention___下_.mp4  71.41M

├──25__选修_To_Learn_More___Unsupervised_Learning___Word_Embedding.mp4  67.75M

├──26__选修_To_Learn_More___Spatial_Transformer_Layer.mp4  53.57M

├──27__选修_To_Learn_More___Recurrent_Neural_Network.mp4  68.69M

├──28__选修_To_Learn_More___Graph_Neural_Network_1_2_.mp4  76.77M

├──29__选修_To_Learn_More___Graph_Neural_Network_2_2_.mp4  137.64M

├──30_作业说明_HW3_中文低画质.mp4  57.16M

├──31_作业说明_HW3_英文高画质有字幕.mp4  61.91M

├──32_作业说明_HW4_中文低画质版.mp4  55.11M

├──33_作业说明_HW4_英文无字幕高清版.mp4  49.18M

├──34_第五节_Transformer__上_.mp4  53.07M

├──35_Transformer__下_.mp4  105.63M

├──36__选修_To_Learn_More___Non_Autoregressive_Sequence_Generation.mp4  117.95M

├──37_作业说明_HW5_中文___Judgeboi讲解.mp4  109.24M

├──38_作业说明_HW5_slides_tutorial__英文版机翻.mp4  30.43M

├──39_作业说明_HW5_code_tutorial__英文版机翻.mp4  53.51M

├──40_第六节_生成式对抗网络_GAN___一____基本概念介紹.mp4  66.54M

├──41_生成式对抗网络_GAN___二____理论介绍与WGAN.mp4  74.76M

├──42_生成式对抗网络_GAN___三____生成器效能评估与条件式生成.mp4  83.72M

├──43_生成式对抗网络_GAN___四____Cycle_GAN.mp4  42.75M

├──44__选修_To_Learn_More___Unsupervised_Learning___Deep_Generative_Model__Part_I_.mp4  45.33M

├──45__选修_To_Learn_More___Unsupervised_Learning___Deep_Generative_Model__Part_II_.mp4  100.67M

├──46__选修_To_Learn_More___Flow_based__Generative_Model.mp4  58.96M

├──47_作业说明_HW6_中文版低画质.mp4  42.51M

├──48_作业说明_HW6_英文版高画质有字幕.mp4  28.97M

├──49_第七节_自监督式学习___一____芝麻街与进击的巨人.mp4  14.37M

├──50_自监督式学习__二____BERT简介.mp4  77.34M

├──51_自监督式学习__三_____BERT的奇闻轶事.mp4  37.53M

├──52_自监督式学习__四____GPT的野望.mp4  30.47M

├──53_自编码器__Auto_encoder___上____基本概念.mp4  33.72M

├──54_自编码器__Auto_encoder___下____领结变声器与更多应用.mp4  50.45M

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└──Lhy_Machine_Learning-main.zip  306.87M