├── .gitignore ├── README.md ├── SUMMARY.md ├── books └── deep-work.md ├── courses ├── coursera │ └── machine-learning │ │ ├── README.md │ │ ├── week-1 │ │ └── introduction.md │ │ └── week-2 │ │ └── linear-regression-with-multiple-variables.md ├── courses.md └── fast.ai │ ├── deep-learning-part-1 │ ├── 2018-edition │ │ └── README.md │ └── 2019-edition │ │ ├── README.md │ │ ├── lesson-1-image-recognition.md │ │ ├── lesson-2-deeper-dive-into-cv.md │ │ ├── lesson-3-multilabel-segmentation.md │ │ ├── lesson-4-nlp-tabular-collab.md │ │ ├── lesson-5-foundations-neural-nets.md │ │ ├── lesson-6-foundations-convolutional-neural-nets.md │ │ └── lesson-7-resnet-unet-gan-rnn.md │ ├── deep-learning-part-2 │ ├── 2017-edition │ │ ├── README.md │ │ ├── lesson-10-multi-modal-and-gans.md │ │ ├── lesson-11-memory-networks.md │ │ ├── lesson-12-attentional-models.md │ │ ├── lesson-13-neural-translation.md │ │ ├── lesson-14-time-series-and-segmentation.md │ │ ├── lesson-8-artistic-style.md │ │ └── lesson-9-generative-models.md │ └── 2018-edition │ │ ├── README.md │ │ ├── lesson-10-transfer-learning-nlp.md │ │ ├── lesson-11-neural-translation.md │ │ ├── lesson-12-gan.md │ │ ├── lesson-13-image-enhancement.md │ │ ├── lesson-14-image-segmentation.md │ │ ├── lesson-8-object-detection.md │ │ └── lesson-9-multi-object-detection.md │ ├── fast.ai.md │ └── machine-learning │ └── 2017-edition │ ├── README.md │ ├── lesson-1-intro-random-forests.md │ ├── lesson-2-random-forest-deep-dive.md │ ├── lesson-3-feature-engineering.md │ ├── lesson-4-random-forest-interpretation.md │ └── lesson-5-train-vs-test.md ├── images ├── devise_notebook_001.png ├── devise_notebook_002.png ├── devise_notebook_003.png ├── devise_notebook_004.png ├── devise_notebook_005.png ├── devise_notebook_006.png ├── fastai_p1_v3 │ ├── lesson_1 │ │ ├── 1.png │ │ ├── 10.png │ │ ├── 100.png │ │ ├── 102.png │ │ ├── 103.png │ │ ├── 104.png │ │ ├── 105.png │ │ ├── 106.png │ │ ├── 107.png │ │ ├── 108.png │ │ ├── 109.png │ │ ├── 11.png │ │ ├── 110.png │ │ ├── 111.png │ │ ├── 112.png │ │ ├── 113.png │ │ ├── 114.png │ │ ├── 115.png │ │ ├── 116.png │ │ ├── 117.png │ │ ├── 118.png │ │ ├── 119.png │ │ ├── 12.png │ │ ├── 120.png │ │ ├── 121.png │ │ ├── 122.png │ │ ├── 123.png │ │ ├── 124.png │ │ ├── 125.png │ │ ├── 126.png │ │ ├── 127.png │ │ ├── 128.png │ │ ├── 129.png │ │ ├── 13.png │ │ ├── 130.png │ │ ├── 131.png │ │ ├── 132.jpg │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ ├── 9.png │ │ ├── c1.png │ │ ├── fastai_lib.png │ │ └── oxford_iiit_pets_paper.png │ ├── lesson_2 │ │ ├── 1.png │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 12.png │ │ ├── 13.png │ │ ├── 14.png │ │ ├── 15.png │ │ ├── 16.png │ │ ├── 17.png │ │ ├── 18.png │ │ ├── 19.png │ │ ├── 2.png │ │ ├── 20.png │ │ ├── 21.png │ │ ├── 22.png │ │ ├── 23.png │ │ ├── 24.png │ │ ├── 25.png │ │ ├── 26.png │ │ ├── 27.png │ │ ├── 28.png │ │ ├── 29.png │ │ ├── 3.png │ │ ├── 30.png │ │ ├── 31.png │ │ ├── 32.png │ │ ├── 33.png │ │ ├── 34.png │ │ ├── 35.png │ │ ├── 36.png │ │ ├── 37.png │ │ ├── 38.png │ │ ├── 39.png │ │ ├── 4.png │ │ ├── 40.png │ │ ├── 41.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ ├── 9.png │ │ ├── adam.gif │ │ ├── bear.png │ │ ├── bears.png │ │ ├── download.mp4 │ │ ├── lr.gif │ │ ├── matrix.gif │ │ ├── matrix.png │ │ ├── n1.png │ │ ├── n2.png │ │ ├── n3.png │ │ └── rachel.png │ ├── lesson_3 │ │ ├── 1.png │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 12.png │ │ ├── 13.png │ │ ├── 14.png │ │ ├── 15.png │ │ ├── 16.png │ │ ├── 17.png │ │ ├── 18.png │ │ ├── 19.png │ │ ├── 2.png │ │ ├── 20.png │ │ ├── 21.png │ │ ├── 22.png │ │ ├── 23.png │ │ ├── 24.png │ │ ├── 25.png │ │ ├── 26.png │ │ ├── 27.png │ │ ├── 28.png │ │ ├── 29.png │ │ ├── 3.png │ │ ├── 30.png │ │ ├── 31.png │ │ ├── 32.png │ │ ├── 33.png │ │ ├── 34.png │ │ ├── 35.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ ├── 9.png │ │ ├── b0.png │ │ ├── b1.png │ │ ├── b2.png │ │ ├── b3.png │ │ ├── b4.png │ │ ├── c1.png │ │ ├── c2.png │ │ ├── c3.png │ │ ├── c4.png │ │ ├── c5.png │ │ ├── c6.png │ │ ├── c7.png │ │ ├── c8.png │ │ ├── c9.png │ │ ├── drawing1.gif │ │ ├── drawing2.gif │ │ ├── jose1.gif │ │ ├── jose2.gif │ │ ├── jose3.gif │ │ ├── jose4.gif │ │ ├── jose5.gif │ │ ├── mnist.png │ │ ├── n1.png │ │ ├── n2.png │ │ ├── n3.png │ │ ├── n4.png │ │ ├── planet.png │ │ ├── sagar.png │ │ └── u-net-architecture.png │ ├── lesson_4 │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 12.png │ │ ├── 13.png │ │ ├── 14.png │ │ ├── 15.png │ │ ├── 16.png │ │ ├── 17.png │ │ ├── 18.png │ │ ├── 19.png │ │ ├── 2.png │ │ ├── 20.png │ │ ├── 21.png │ │ ├── 22.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png │ ├── lesson_5 │ │ ├── 1.png │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 12.png │ │ ├── 13.png │ │ ├── 14.png │ │ ├── 15.png │ │ ├── 16.png │ │ ├── 17.png │ │ ├── 18.png │ │ ├── 19.png │ │ ├── 2.png │ │ ├── 20.png │ │ ├── 21.png │ │ ├── 22.png │ │ ├── 23.png │ │ ├── 24.png │ │ ├── 25.png │ │ ├── 26.png │ │ ├── 27.png │ │ ├── 28.png │ │ ├── 29.png │ │ ├── 3.png │ │ ├── 30.png │ │ ├── 31.png │ │ ├── 32.png │ │ ├── 33.png │ │ ├── 34.png │ │ ├── 35.png │ │ ├── 36.png │ │ ├── 37.png │ │ ├── 38.png │ │ ├── 39.png │ │ ├── 4.png │ │ ├── 40.png │ │ ├── 41.png │ │ ├── 42.png │ │ ├── 43.png │ │ ├── 44.png │ │ ├── 45.png │ │ ├── 46.png │ │ ├── 47.png │ │ ├── 48.png │ │ ├── 49.png │ │ ├── 5.png │ │ ├── 50.png │ │ ├── 51.png │ │ ├── 52.png │ │ ├── 53.png │ │ ├── 54.png │ │ ├── 55.png │ │ ├── 56.png │ │ ├── 57.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ ├── 9.png │ │ ├── embedding.png │ │ └── momentum.gif │ ├── lesson_6 │ │ ├── 1.png │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 12.png │ │ ├── 13.png │ │ ├── 14.png │ │ ├── 15.png │ │ ├── 16.png │ │ ├── 17.png │ │ ├── 18.png │ │ ├── 19.png │ │ ├── 2.png │ │ ├── 20.png │ │ ├── 21.png │ │ ├── 22.png │ │ ├── 23.png │ │ ├── 24.png │ │ ├── 25.png │ │ ├── 26.png │ │ ├── 27.png │ │ ├── 28.png │ │ ├── 29.png │ │ ├── 3.png │ │ ├── 30.png │ │ ├── 31.png │ │ ├── 32.png │ │ ├── 33.png │ │ ├── 34.png │ │ ├── 35.png │ │ ├── 36.png │ │ ├── 37.png │ │ ├── 38.png │ │ ├── 39.png │ │ ├── 4.png │ │ ├── 40.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ ├── 9.png │ │ └── image_kernels.gif │ └── lesson_7 │ │ ├── 1.png │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 12.png │ │ ├── 13.png │ │ ├── 14.png │ │ ├── 15.png │ │ ├── 16.png │ │ ├── 17.png │ │ ├── 18.png │ │ ├── 19.png │ │ ├── 2.png │ │ ├── 20.png │ │ ├── 21.png │ │ ├── 22.png │ │ ├── 23.png │ │ ├── 24.png │ │ ├── 25.png │ │ ├── 26.png │ │ ├── 27.png │ │ ├── 29.png │ │ ├── 3.png │ │ ├── 33.png │ │ ├── 4.png │ │ ├── 41.png │ │ ├── 42.png │ │ ├── 43.png │ │ ├── 44.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png ├── imdb_notebook_001.png ├── imdb_notebook_002.png ├── imdb_notebook_003.png ├── imdb_notebook_004.png ├── imdb_notebook_005.png ├── imdb_notebook_006.png ├── imdb_notebook_007.png ├── imdb_notebook_008.png ├── imdb_notebook_009.png ├── imdb_notebook_010.png ├── imdb_notebook_011.png ├── imdb_notebook_012.png ├── imdb_notebook_013.png ├── imdb_notebook_014.png ├── imdb_notebook_015.png ├── imdb_notebook_016.png ├── imdb_notebook_017.png ├── imdb_notebook_018.png ├── imdb_notebook_019.png ├── imdb_notebook_020.png ├── imdb_notebook_021.png ├── imdb_notebook_022.png ├── imdb_notebook_023.png ├── imdb_notebook_024.png ├── imdb_notebook_025.png ├── imdb_notebook_026.png ├── imdb_notebook_027.png ├── imdb_notebook_028.png ├── imdb_notebook_029.png ├── imdb_notebook_030.png ├── imdb_notebook_031.png ├── imdb_notebook_032.png ├── imdb_notebook_033.png ├── imdb_notebook_034.png ├── imdb_notebook_035.png ├── imdb_notebook_036.png ├── imdb_notebook_037.png ├── imdb_notebook_038.png ├── lesson_12_001.png ├── lesson_12_002.png ├── lesson_12_003.png ├── lesson_12_004.png ├── lesson_12_005.png ├── lesson_12_006.png ├── lesson_12_007.png ├── lesson_12_008.png ├── lesson_12_009.png ├── lesson_12_010.png ├── lesson_12_011.png ├── lesson_12_012.png ├── lesson_12_013.png ├── lesson_12_014.png ├── lesson_12_015.png ├── lesson_12_016.png ├── lesson_12_017.png ├── lesson_12_018.png ├── lesson_12_019.png ├── lesson_12_020.png ├── lesson_12_021.png ├── lesson_12_022.png ├── lesson_12_023.png ├── lesson_12_024.png ├── lesson_12_025.png ├── lesson_12_026.png ├── lesson_12_027.png ├── lesson_12_028.png ├── lesson_12_029.png ├── lesson_12_030.png ├── lesson_12_031.png ├── lesson_12_032.gif ├── lesson_12_033.jpeg ├── lesson_12_034.jpeg ├── lesson_12_035.jpeg ├── lesson_12_036.jpeg ├── lesson_12_037.png ├── lesson_12_038.png ├── lesson_12_039.png ├── lesson_12_040.png ├── lesson_12_041.png ├── lesson_12_042.png ├── lesson_12_043.png ├── lesson_12_044.png ├── lesson_12_045.png ├── lesson_12_046.png ├── lesson_12_047.png ├── lesson_12_048.png ├── lesson_12_049.png ├── lesson_12_050.png ├── lesson_12_051.png ├── lesson_12_052.png ├── lesson_12_053.png ├── lesson_12_054.png ├── lesson_12_055.png ├── lesson_12_056.png ├── lesson_12_057.png ├── lesson_12_058.png ├── lesson_12_059.png ├── lesson_12_060.png ├── lesson_12_061.png ├── lesson_12_062.png ├── lesson_12_063.png ├── lesson_12_064.png ├── lesson_12_065.png ├── lesson_12_066.png ├── lesson_12_067.png ├── lesson_12_068.png ├── lesson_12_069.png ├── lesson_12_070.png ├── lesson_13_001.png ├── lesson_13_002.png ├── lesson_13_003.png ├── lesson_13_004.png ├── lesson_13_005.png ├── lesson_13_006.png ├── lesson_13_007.png ├── lesson_13_008.png ├── lesson_13_009.png ├── lesson_13_010.png ├── lesson_13_011.png ├── lesson_13_012.png ├── lesson_13_013.png ├── lesson_13_014.png ├── lesson_13_015.png ├── lesson_13_016.png ├── lesson_13_017.png ├── lesson_13_018.png ├── lesson_13_019.png ├── lesson_13_020.png ├── lesson_13_021.png ├── lesson_13_022.png ├── lesson_13_023.png ├── lesson_13_024.png ├── lesson_13_025.png ├── lesson_13_026.png ├── lesson_13_027.png ├── lesson_13_028.png ├── lesson_13_029.png ├── lesson_13_030.png ├── lesson_13_031.png ├── lesson_13_032.png ├── lesson_13_033.png ├── lesson_13_034.png ├── lesson_13_035.png ├── lesson_13_036.png ├── lesson_13_037.png ├── lesson_13_038.png ├── lesson_13_039.png ├── lesson_13_039_1.png ├── lesson_13_040.png ├── lesson_13_041.png ├── lesson_13_042.png ├── lesson_13_043.png ├── lesson_13_044.png ├── lesson_13_045.png ├── lesson_13_046.png ├── lesson_13_047.png ├── lesson_13_048.png ├── lesson_13_049.png ├── lesson_13_050.png ├── lesson_13_051.png ├── lesson_13_052.png ├── lesson_13_053.png ├── lesson_13_054.png ├── lesson_13_055.png ├── lesson_14_001.png ├── lesson_14_002.jpeg ├── lesson_14_003.png ├── lesson_14_004.png ├── lesson_14_005.png ├── lesson_14_006.png ├── lesson_14_007.png ├── lesson_14_008.png ├── lesson_14_009.png ├── lesson_14_010.png ├── lesson_14_011.png ├── lesson_14_012.png ├── lesson_14_013.png ├── lesson_14_014.png ├── lesson_14_015.png ├── lesson_14_016.png ├── lesson_14_017.png ├── lesson_14_018.png ├── lesson_14_019.png ├── lesson_14_020.png ├── lesson_14_021.png ├── lesson_14_022.png ├── lesson_14_023.png ├── lesson_14_024.png ├── lesson_14_025.png ├── lesson_14_026.png ├── lesson_14_027.png ├── lesson_14_028.png ├── lesson_14_029.png ├── lesson_14_030.png ├── lesson_14_031.png ├── lesson_14_032.png ├── lesson_14_033.png ├── lesson_14_034.png ├── lesson_14_035.png ├── lesson_14_036.png ├── lesson_14_037.png ├── lesson_14_038.png ├── lesson_14_039.jpg ├── lesson_14_040.png ├── lesson_14_041.png ├── lesson_14_042.png ├── lesson_14_043.png ├── lesson_14_044.png ├── lesson_14_045.png ├── lesson_14_046.png ├── lesson_14_047.png ├── lesson_14_048.png ├── lesson_14_049.png ├── lesson_14_050.png ├── lesson_14_051.png ├── lesson_14_052.png ├── lesson_14_053.png ├── lesson_14_054.png ├── lesson_14_055.png ├── lesson_14_056.png ├── lesson_14_057.png ├── lesson_14_058.png ├── lesson_14_059.png ├── lesson_14_060.png ├── lesson_14_061.png ├── lesson_14_062.png ├── lesson_14_063.png ├── lesson_14_064.png ├── lesson_14_065.png ├── lesson_14_066.png ├── lesson_14_067.png ├── lesson_14_068.png ├── lesson_14_069.png ├── lesson_14_070.png ├── lesson_14_071.png ├── lesson_14_072.png ├── lesson_14_073.png ├── lesson_14_074.png ├── lesson_14_075.png ├── lesson_14_076.png ├── lesson_14_077.png ├── lesson_14_078.png ├── lesson_14_079.png ├── lesson_14_080.png ├── lesson_14_081.png ├── lesson_14_082.png ├── lesson_14_083.png ├── lesson_14_084.png ├── lesson_14_085.png ├── ml_2017_lesson_1_001.png ├── ml_2017_lesson_1_002.png ├── ml_2017_lesson_1_003.png ├── ml_2017_lesson_1_004.png ├── ml_2017_lesson_1_005.png ├── ml_2017_lesson_1_006.png ├── ml_2017_lesson_1_007.png ├── ml_2017_lesson_1_008.png ├── ml_2017_lesson_1_009.png ├── ml_2017_lesson_1_010.png ├── ml_2017_lesson_2_001.png ├── ml_2017_lesson_2_002.png ├── ml_2017_lesson_2_003.png ├── ml_2017_lesson_2_004.png ├── ml_2017_lesson_2_005.png ├── ml_2017_lesson_2_006.png ├── ml_2017_lesson_2_007.png ├── ml_2017_lesson_2_008.png ├── ml_2017_lesson_2_009.png ├── ml_2017_lesson_2_010.png ├── ml_2017_lesson_3_001.png ├── ml_2017_lesson_3_002.png ├── ml_2017_lesson_3_003.png ├── ml_2017_lesson_3_004.png ├── ml_2017_lesson_3_005.png ├── ml_2017_lesson_3_006.png ├── ml_2017_lesson_3_007.png ├── ml_2017_lesson_3_008.png ├── ml_2017_lesson_3_009.png ├── ml_2017_lesson_3_010.png ├── ml_2017_lesson_3_011.png ├── ml_2017_lesson_3_012.png ├── ml_2017_lesson_3_013.png ├── ml_2017_lesson_3_014.png ├── ml_2017_lesson_3_015.png ├── ml_2017_lesson_3_016.png ├── ml_2017_lesson_3_017.png ├── ml_2017_lesson_3_018.png ├── ml_2017_lesson_4_001.png ├── ml_2017_lesson_4_002.png ├── ml_2017_lesson_4_003.png ├── ml_2017_lesson_4_004.png ├── ml_2017_lesson_4_005.png ├── ml_2017_lesson_4_006.png ├── ml_2017_lesson_4_007.png ├── ml_2017_lesson_4_008.png ├── ml_2017_lesson_4_009.png ├── ml_2017_lesson_4_010.png ├── ml_2017_lesson_4_011.png ├── ml_2017_lesson_4_012.png ├── ml_2017_lesson_4_013.png ├── ml_2017_lesson_4_014.png ├── ml_2017_lesson_4_015.png ├── ml_2017_lesson_4_016.png ├── ml_2017_lesson_4_017.png ├── ml_2017_lesson_4_018.png ├── ml_2017_lesson_4_019.png ├── ml_2017_lesson_4_020.png ├── ml_2017_lesson_4_021.png ├── ml_2017_lesson_4_022.png ├── ml_2017_lesson_4_023.png ├── ml_2017_lesson_4_024.png ├── ml_2017_lesson_4_025.png ├── ml_2017_lesson_4_026.png ├── ml_2017_lesson_4_027.png ├── ml_2017_lesson_4_028.png ├── ml_2017_lesson_4_029.png ├── pascal_multi_notebook_4_by_4_grid_from_fin_conv_layer.png ├── pascal_multi_notebook__more_anchors_training_res_plot.png ├── pascal_multi_notebook_activations_to_bbox_plot.png ├── pascal_multi_notebook_focal_loss_training_res.png ├── pascal_multi_notebook_full_model_res_01.png ├── pascal_multi_notebook_full_model_res_02.png ├── pascal_multi_notebook_full_model_res_03.png ├── pascal_multi_notebook_full_model_res_04.png ├── pascal_multi_notebook_full_model_res_05.png ├── pascal_multi_notebook_full_model_res_06.png ├── pascal_multi_notebook_full_model_res_07.png ├── pascal_multi_notebook_full_model_res_08.png ├── pascal_multi_notebook_full_model_res_09.png ├── pascal_multi_notebook_full_model_res_10.png ├── pascal_multi_notebook_goal_dep_vars_fin_layer_loss_fn.png ├── pascal_multi_notebook_grid.png ├── pascal_multi_notebook_ground_truth.png ├── pascal_multi_notebook_img_classification_plot.png ├── pascal_multi_notebook_matching_stage_result.png ├── pascal_multi_notebook_more_anchors.png ├── pascal_multi_notebook_possible_arch_det_16_obj.png ├── pascal_multi_receptive_field_excel_1.png ├── pascal_multi_receptive_field_excel_2.png ├── pascal_notebook_data_aug_wrong_bbox.png ├── pascal_notebook_img_classi_plot.png ├── pascal_notebook_model_summary.png ├── pascal_notebook_obj_det_all_img_bbox_plot.png ├── pascal_notebook_obj_det_one_img_bbox_plot.png ├── pascal_notebook_single_obj_det.png ├── pascal_notebook_single_obj_det_train_results.png ├── ssd_multihead_linebyline.png ├── translate_notebook_001.png ├── translate_notebook_002.png ├── translate_notebook_003.png ├── translate_notebook_004.png ├── translate_notebook_005.png ├── translate_notebook_006.png ├── translate_notebook_007.png ├── translate_notebook_008.png ├── translate_notebook_009.png ├── translate_notebook_010.png ├── translate_notebook_011.png ├── translate_notebook_012.png ├── translate_notebook_013.png ├── translate_notebook_014.png ├── translate_notebook_015.png ├── translate_notebook_016.png ├── translate_notebook_017.png ├── translate_notebook_018.png ├── translate_notebook_019.png ├── translate_notebook_020.png ├── translate_notebook_021.png ├── translate_notebook_022.png ├── translate_notebook_023.png ├── translate_notebook_024.png ├── translate_notebook_025.png ├── translate_notebook_026.png ├── translate_notebook_027.png ├── translate_notebook_028.png ├── translate_notebook_029.png └── translate_notebook_030.png └── programming-languages ├── javascript ├── javascript.md └── js-libraries │ └── js-libraries.md └── programming-languages.md /.gitignore: -------------------------------------------------------------------------------- 1 | # Editors artefacts 2 | .vscode/ -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | > This wiki is now archived - look at [wiki.cedricchee.com (InfoWarehouse)](https://wiki.cedricchee.com/) if you would like an updated wiki. Thank you. 2 | 3 | # My Knowledge Wiki 4 | 5 | Hey, this is a place where I **document everything I know** and **learn** so far about the world in the form of an **online book**. I am sharing them in the hope that they would be useful to you as well. 6 | 7 | > "An investment in knowledge pays the best interest." 8 | > 9 | > _— Ben Franklin_ 10 | 11 | ## How to use the wiki effectively? 12 | 13 | In the left corner, you can make searches for what content I have already indexed or you can simply explore the tree view to the left. 14 | 15 | ## What kinds of contents 16 | 17 | I want to remember more [of what I read](http://austinkleon.com/2016/03/03/how-to-read-more-2/), so I take notes to share [compressed wisdom](https://sivers.org/2do). 18 | 19 | ## What kinds of notes 20 | 21 | * Notes on various computer science \(technical\) topics. 22 | * General notes. 23 | * Notes from projects that I've work on. 24 | * Notes from courses \(educational materials\) that I've study. 25 | * Notes from books and other interesting things that I've read. 26 | 27 | ## Other things I wrote and shared 28 | 29 | I share my knowledge in a longer form by writing [articles](https://medium.com/@cedric_chee). I also love writing code and share it all on [GitHub](https://github.com/cedrickchee). You can find all the things I have made and shared thus far [here](https://cedricchee/projects). 30 | 31 | ## Contributing 32 | 33 | If you found a mistake anywhere in this wiki, I would really appreciate your help. You can quickly find any entry you wish to edit by [searching for the topic](https://github.com/cedrickchee/knowledge/find/master) and then making the changes. 34 | 35 | ## License 36 | 37 | * Code: [MIT](https://cedrickchee.mit-license.org/) © Cedric Chee 38 | * Content: [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/) 39 | 40 | 41 | 42 | -------------------------------------------------------------------------------- /courses/coursera/machine-learning/README.md: -------------------------------------------------------------------------------- 1 | # Machine Learning 2 | 3 | [Course website](https://www.coursera.org/learn/machine-learning) 4 | 5 | -------------------------------------------------------------------------------- /courses/coursera/machine-learning/week-1/introduction.md: -------------------------------------------------------------------------------- 1 | # Introduction 2 | 3 | Welcome to Machine Learning! In this module, we introduce the core idea of teaching a computer to learn concepts using data—without being explicitly programmed. The Course Wiki is under construction. Please visit the resources tab for the most complete and up-to-date information. 4 | 5 | -------------------------------------------------------------------------------- /courses/coursera/machine-learning/week-2/linear-regression-with-multiple-variables.md: -------------------------------------------------------------------------------- 1 | # Linear Regression with Multiple Variables 2 | 3 | What if your input has more than one value? In this module, we show how linear regression can be extended to accommodate multiple input features. We also discuss best practices for implementing linear regression. 4 | 5 | -------------------------------------------------------------------------------- /courses/courses.md: -------------------------------------------------------------------------------- 1 | # Courses 2 | 3 | In this collection, you will find my notes for all the courses I am currently studying or have completed. The courses are from [MOOC](https://en.wikipedia.org/wiki/Massive_open_online_course) providers and online learning platforms. An incomplete list below: 4 | 5 | * [Coursera](https://www.coursera.org) 6 | * [Udacity](https://www.udacity.com/) 7 | * [Stanford Online](https://online.stanford.edu/) 8 | * [fast.ai](http://www.fast.ai) 9 | * [deeplearning.ai](https://www.deeplearning.ai/) 10 | * [edX](https://www.edx.org) 11 | * [Khan Academy](https://www.khanacademy.org/) 12 | * [Udemy](https://www.udemy.com/) 13 | 14 | I keep a [Trello board](https://trello.com/b/q49ybAYJ/learning-courses-books) of what I am learning and plan to learn. You can see the: 15 | - courses I am taking and plan to take. 16 | - books I am reading and plan to read. -------------------------------------------------------------------------------- /courses/fast.ai/deep-learning-part-1/2018-edition/README.md: -------------------------------------------------------------------------------- 1 | # Deep Learning Part 1: 2018 Edition 2 | 3 | ## Course Materials 4 | 5 | * Blog post 6 | * [Practical Deep Learning for Coders 2018](http://www.fast.ai/2018/01/26/v2-launch/) 7 | * [Website](http://course.fast.ai/) -------------------------------------------------------------------------------- /courses/fast.ai/deep-learning-part-1/2019-edition/README.md: -------------------------------------------------------------------------------- 1 | # Deep Learning Part 1: Version 3 / 2019 Edition 2 | 3 | ## Course Materials 4 | 5 | * [Application Announcement](http://forums.fast.ai/t/fast-ai-live-the-new-version-of-the-international-fellowship/22825): closed 6 | * [Website](http://course.fast.ai/) (officially released in early 2019) 7 | 8 | The 3rd edition of course.fast.ai - coming in 2019. This 7-week course is designed for anyone with at least a year of coding experience, and some memory of high-school math. The course is taught in Python, using the fastai library and PyTorch. You will start with step one—learning how to get a GPU server online suitable for deep learning—and go all the way through to creating state of the art, highly practical, models for computer vision, natural language processing, and recommendation systems. 9 | 10 | * [Forum (suitable for people who have not studied deep learning before)](https://forums.fast.ai/c/part1-v3) 11 | * [Forum for advanced questions](https://forums.fast.ai/c/part1-v3/part1-v3-adv) 12 | * [FAQ, resources, and official course updates](https://forums.fast.ai/t/faq-resources-and-official-course-updates/27934) 13 | * [Jupyter Notebook and Code](https://github.com/fastai/course-v3/tree/master/nbs/dl1) 14 | 15 | ## Lessons Cover 16 | 17 | Many topics, including: 18 | 19 | * image recognition 20 | * multi-label image classification 21 | * different kind of images 22 | * Convolutional Neural Networks (CNNs) 23 | * image segmentation with U-Net 24 | * overfitting 25 | * embeddings 26 | * collaborative filtering: recommendation systems 27 | * Natural Language Processing (NLP) 28 | * language model, sentiment analysis 29 | * text classification 30 | * Recurrent Neural Networks (RNNs) 31 | * RNN architecture from scratch 32 | * tabular/structured data 33 | * time-series prediction using neural network 34 | * CNN architecture 35 | * back to computer vision 36 | * CNN in-depth and ResNets from scratch -------------------------------------------------------------------------------- /courses/fast.ai/deep-learning-part-2/2017-edition/README.md: -------------------------------------------------------------------------------- 1 | # Deep Learning Part 2: 2017 Edition 2 | 3 | ## Course Materials 4 | 5 | * Blog post 6 | * [Cutting Edge Deep Learning for Coders: Launching Deep Learning Part 2](http://www.fast.ai/2017/07/28/deep-learning-part-two-launch/) 7 | * [Practical Deep Learning Part 2 - Integrating Recent Advances and Classic Machine Learning](http://www.fast.ai/2017/01/17/curriculum2/) 8 | * [Website](http://course17.fast.ai/part2.html) 9 | 10 | You'll learn the latest developments in deep learning, how to read and implement new academic papers, and how to solve challenging end-to-end problems such as natual language translation. 11 | 12 | * [Wiki](http://forums.fast.ai/c/part2) 13 | * [Jupyter Notebook and Code](https://github.com/fastai/courses/tree/master/deeplearning2) 14 | * [Complete Collection of Video Timelines](http://forums.fast.ai/t/part-2-complete-collection-of-video-timelines/4933) 15 | 16 | -------------------------------------------------------------------------------- /courses/fast.ai/deep-learning-part-2/2017-edition/lesson-12-attentional-models.md: -------------------------------------------------------------------------------- 1 | # Lesson 12 - Attentional Models 2 | 3 | Topics: 4 | 5 | * Clustering in TensorFlow 6 | * Attentional models 7 | 8 | Lesson 9 | 10 | * [Website](http://course17.fast.ai/lessons/lesson12.html) 11 | * [Video](https://youtu.be/jy1w0mPCHb0) 12 | * [Wiki](http://forums.fast.ai/t/lesson-12-wiki) 13 | * [Forum discussion](http://forums.fast.ai/t/lesson-12-discussion) 14 | 15 | ## Coursework 16 | 17 | ### Jupyter Notebook Used 18 | 19 | * \[time: 00:00:00\] [kmeans\_test.ipynb](https://github.com/fastai/courses/blob/master/deeplearning2/kmeans_test.ipynb) 20 | * \[time: 00:40:00\] [spelling\_bee\_RNN.ipynb](https://github.com/fastai/courses/blob/master/deeplearning2/spelling_bee_RNN.ipynb) 21 | 22 | ### Reading: Paper \[TODO\] 23 | 24 | * \[time: 01:37:28\] [Neural Machine Translation by Jointly Learning to Align and Translate](https://arxiv.org/abs/1409.0473) by Dzmitry Bahdanau, et. al 25 | * \[time: 01:44:08\] [Grammar as a Foreign Language](https://arxiv.org/abs/1412.7449) by Oriol Vinyals et. al 26 | * [Massive Exploration of Neural Machine Translation Architectures](https://arxiv.org/abs/1703.03906) 27 | * This is a very recent research paper on the subject \(released last week 21 Mar 2017\) 28 | * Open source code: [https://github.com/google/seq2seq/](https://github.com/google/seq2seq/) 29 | 30 | ## My Notes 31 | 32 | * \[time: 00:00:00\] Clustering again. Kmeans. 33 | * \[time: 00:40:00\] Intro to next step: NLP and translation deep-dive, with CMU pronouncing dictionary via `spelling_bee_RNN.ipynb` 34 | * \[time: 01:32:00\] Attention models. I actually really like these, I think they're great. And really the paper that introduced these, quite an extraordinary paper, introduced both GRUs and attention models at the same time. I think it might even be before the guy had his PhD, if I remember correctly. It was just a wonderful paper, very successful. 35 | * \[time: 01:28:38\] Even though I try to teach things which I think are going to stand the test of time, I'm not at all convinced that any technique for reinforcement learning is going to stand the test of time. So I don't think we're going to touch that. 36 | * \[time: 01:44:08\] This whole thing is summarized in another paper, actually a very cool paper, Grammar as a Foreign Language. Lots of names you probably recognize here, Geoffrey Hinton, who's kind of the father of deep learning; Illya Stuskever, who's Director of Science at OpenAI; Oriel Vinyals, who's done lots of cool stuff. This paper is kind of neat and fun anyway. It basically says, What if you didn't know anything about grammar and you attempted to build a neural net which assigned grammar to sentences. It turns out you actually end up with something more accurate than any rule-based grammar system that's been built. 37 | * \[time: 01:49:50\] AMA - Question: Any advice on imbalanced datasets? Seems to be a recurring issue with real world data. 38 | 39 | -------------------------------------------------------------------------------- /courses/fast.ai/deep-learning-part-2/2018-edition/README.md: -------------------------------------------------------------------------------- 1 | # Deep Learning Part 2: 2018 Edition 2 | 3 | ## Course Materials 4 | 5 | * [Blog post](http://www.fast.ai/2018/05/07/part2-launch/) 6 | * [Website](http://course.fast.ai/part2.html) 7 | 8 | You'll learn the latest developments in deep learning, how to read and implement new academic papers, and how to solve challenging end-to-end problems such as natural language translation. You'll develop a deep understanding of neural network foundations, the most important recent advances in the fields, and how to implement them in the [world's fastest deep learning libraries](http://www.fast.ai/2018/04/30/dawnbench-fastai/), fastai and PyTorch. 9 | 10 | * [Wiki](http://forums.fast.ai/c/part2-v2) 11 | * [Jupyter Notebook and Code](https://github.com/fastai/fastai/tree/master/courses/dl2) 12 | 13 | ## Lessons Cover 14 | 15 | Many topics, including: 16 | 17 | * multi-object detection with SSD and YOLOv3 18 | * how to read academic papers 19 | * customizing a pre-trained model with a custom head 20 | * more complex data augmentation \(for coordinate variables, per-pixel classification, etc\) 21 | * NLP transfer learning 22 | * handling very large \(billion+ token\) text corpuses with the new fastai.text library 23 | * running and interpreting ablation studies 24 | * state of the art NLP classification 25 | * multi-modal learning 26 | * multi-task learning 27 | * bidirectional LSTM with attention for seq2seq 28 | * neural translation 29 | * customizing resnet architectures 30 | * GANs, WGAN, and CycleGAN 31 | * data ethics 32 | * super resolution 33 | * image segmentation with U-Net 34 | 35 | -------------------------------------------------------------------------------- /courses/fast.ai/machine-learning/2017-edition/README.md: -------------------------------------------------------------------------------- 1 | # Machine Learning: 2017 Edition 2 | 3 | ## Course Materials 4 | 5 | * [Forum](http://forums.fast.ai/t/another-treat-early-access-to-intro-to-machine-learning-videos/6826) 6 | 7 | This course is being taught by Jeremy Howard, and was developed by Jeremy along with Rachel Thomas. Jeremy used a top-down teaching method, which is different from how most math courses operate. If you took the fast.ai deep learning part 1 2018 edition course, that is what we used. The course used a specific library (fastai) built on top of PyTorch. The first part of the course focused on Decision Trees with Random Forests, the second part on Neural Networks for Deep Learning. The course goes at a somewhat gentler pace, but doesn't show how to build world-class models (the focus is more on process and interpretation, and also more in depth discussion of foundational details). The Deep Learning course is more intense, and gets you building state of the art models from lesson 1. Both can be understood on their own, but they both support each other. 8 | 9 | * [Jupyter Notebook and Code](https://github.com/fastai/fastai/tree/master/courses/ml1) 10 | 11 | ## Lessons Cover 12 | 13 | * Introduction to Random Forests 14 | * Random Forest Deep Dive 15 | * Feature Engineering 16 | * Random Forest Interpretation 17 | * Train vs Test 18 | * What is Machine Learning 19 | * Why Do We Use Machine Learning 20 | * Decision Trees Ensemble 21 | * Basic Neural Networks 22 | * SGD 23 | * Backpropagation 24 | * Neural Networkwork Training 25 | * Broadcasting 26 | * PyTorch 27 | * Logistic Regression 28 | * Natural Language Processing (NLP) 29 | * Naive Bayes 30 | * Structured and Time-Series Data 31 | * Entity Embeddings 32 | * Data Science and Ethics 33 | -------------------------------------------------------------------------------- /courses/fast.ai/machine-learning/2017-edition/lesson-5-train-vs-test.md: -------------------------------------------------------------------------------- 1 | # Lesson 5 - Train vs Test 2 | 3 | _These are my personal notes from fast.ai machine learning course and will continue to be updated and improved if I find anything useful and relevant while I continue to review the course to study much more in-depth. Thanks for reading and happy learning!_ 4 | 5 | ## Topics 6 | 7 | * Confidence based on tree variance 8 | 9 | ## Lesson Resources 10 | 11 | * [Video](https://youtu.be/0v93qHDqq_g) 12 | * Jupyter Notebook and code 13 | * [lesson2-rf_interpretation.ipynb](https://nbviewer.jupyter.org/github/fastai/fastai/blob/master/courses/ml1/lesson2-rf_interpretation.ipynb) 14 | * Dataset 15 | * [Blue Book for Bulldozers Kaggle competition](https://www.kaggle.com/c/bluebook-for-bulldozers) 16 | 17 | ## My Notes 18 | 19 | -------------------------------------------------------------------------------- /images/devise_notebook_001.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cedrickchee/knowledge/d3b64458d4918fd8c6552e4016d7866255eec3bb/images/devise_notebook_001.png -------------------------------------------------------------------------------- /images/devise_notebook_002.png: 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1 | # Programming Languages 2 | 3 | According to [Wikipedia](https://en.wikipedia.org/wiki/Programming_language), a programming language is: 4 | 5 | > a formal language that specifies a set of instructions that can be used to produce various kinds of output. Programming languages generally consist of instructions for a computer. Programming languages can be used to create programs that implement specific algorithms. 6 | 7 | --------------------------------------------------------------------------------