# 機器學習及深度學習的資料 ###### tags: `MLDL` # Machine learning **(小推)** 1. https://developers.google.com/machine-learning/crash-course/ google神的ml課程 1. https://ithelp.ithome.com.tw/articles/10206590 1. https://ithelp.ithome.com.tw/users/20107514/ironman/1399 2. https://ithelp.ithome.com.tw/users/20129584/ironman/3370 - https://ithelp.ithome.com.tw/articles/10245037 --- **(如果要實做的話，小推)** > 還是去翻書或上課，因為只是使用sklearn去套的話，大家都會壓! 1. https://buzzorange.com/techorange/2018/08/06/github-100-days-learning/ 印度佬100天機器學習(en) 1. https://github.com/Avik-Jain/100-Days-Of-ML-Code 1. https://zhuanlan.zhihu.com/p/54229077 有人翻譯印度佬的教學 1. https://github.com/MLEveryday/100-Days-Of-ML-Code 簡中翻譯版 --- 1. https://buzzorange.com/techorange/2019/05/17/python-machine-learning-free-course/　另一個完整的ml教學(en) 1. https://machine-learning-course.readthedocs.io/en/latest/index.html 1. https://github.com/machinelearningmindset/machine-learning-course --- 1. https://buzzorange.com/techorange/2017/08/18/learn-machine-learning-and-python-in-14-steps/ **(推)** --- **(導公式，用心推!)** https://medium.com/@chih.sheng.huang821 https://medium.com/@chih.sheng.huang821/%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-%E5%9F%BA%E7%A4%8E%E6%95%B8%E5%AD%B8%E7%AF%87-%E4%B8%80-1c8337179ad6 https://medium.com/@chih.sheng.huang821/%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-%E5%9F%BA%E7%A4%8E%E6%95%B8%E5%AD%B8-%E4%BA%8C-%E6%A2%AF%E5%BA%A6%E4%B8%8B%E9%99%8D%E6%B3%95-gradient-descent-406e1fd001f https://medium.com/@chih.sheng.huang821/%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-%E5%9F%BA%E7%A4%8E%E6%95%B8%E5%AD%B8-%E4%B8%89-%E6%A2%AF%E5%BA%A6%E6%9C%80%E4%BD%B3%E8%A7%A3%E7%9B%B8%E9%97%9C%E7%AE%97%E6%B3%95-gradient-descent-optimization-algorithms-b61ed1478bd7 https://medium.com/@chih.sheng.huang821/%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92%E4%BB%8B%E7%B4%B9-8e49f7f5be29 https://medium.com/@chih.sheng.huang821/%E6%A9%9F%E5%99%A8-%E7%B5%B1%E8%A8%88%E5%AD%B8%E7%BF%92-%E7%BE%85%E5%90%89%E6%96%AF%E5%9B%9E%E6%AD%B8-logistic-regression-aff7a830fb5d https://medium.com/@chih.sheng.huang821/%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-kernel-%E5%87%BD%E6%95%B8-47c94095171 https://medium.com/@chih.sheng.huang821/%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-%E6%94%AF%E6%92%90%E5%90%91%E9%87%8F%E6%A9%9F-support-vector-machine-svm-%E8%A9%B3%E7%B4%B0%E6%8E%A8%E5%B0%8E-c320098a3d2e https://medium.com/@chih.sheng.huang821/%E8%B2%9D%E6%B0%8F%E6%B1%BA%E7%AD%96%E6%B3%95%E5%89%87-bayesian-decision-rule-%E6%9C%80%E5%A4%A7%E5%BE%8C%E9%A9%97%E6%A9%9F%E7%8E%87%E6%B3%95-maximum-a-posterior-map-96625afa19e7 https://medium.com/@chih.sheng.huang821/%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-%E6%B1%BA%E7%AD%96%E6%A8%B9-decision-tree-ed102ee62dfa https://medium.com/@chih.sheng.huang821/%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-%E7%A5%9E%E7%B6%93%E7%B6%B2%E8%B7%AF-%E5%A4%9A%E5%B1%A4%E6%84%9F%E7%9F%A5%E6%A9%9F-multilayer-perceptron-mlp-%E9%81%8B%E4%BD%9C%E6%96%B9%E5%BC%8F-f0e108e8b9af --- **(不錯，推個)** https://medium.com/@yehjames https://medium.com/jameslearningnote/%E8%B3%87%E6%96%99%E5%88%86%E6%9E%90-%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-%E7%AC%AC1-3%E8%AC%9B-kaggle%E4%BB%8B%E7%B4%B9-f0fd99d30f92 https://medium.com/jameslearningnote/%E8%B3%87%E6%96%99%E5%88%86%E6%9E%90-%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-%E7%AC%AC1-4%E8%AC%9B-%E8%B3%87%E6%96%99%E7%A7%91%E5%AD%B8%E9%A0%98%E5%9F%9F%E7%9A%84%E5%A4%A7%E7%A5%9E-%E7%B6%B2%E7%AB%99-ae9577df2c19 https://medium.com/jameslearningnote/%E8%B3%87%E6%96%99%E5%88%86%E6%9E%90-%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-%E7%AC%AC2-1%E8%AC%9B-%E5%A6%82%E4%BD%95%E7%8D%B2%E5%8F%96%E8%B3%87%E6%96%99-sklearn%E5%85%A7%E5%BB%BA%E8%B3%87%E6%96%99%E9%9B%86-baa8f027ed7b https://medium.com/jameslearningnote/%E8%B3%87%E6%96%99%E5%88%86%E6%9E%90-%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-%E7%AC%AC2-2%E8%AC%9B-%E5%A6%82%E4%BD%95%E7%8D%B2%E5%8F%96%E8%B3%87%E6%96%99-google-map-api-beb7c88dc4e3 https://medium.com/jameslearningnote/%E8%B3%87%E6%96%99%E5%88%86%E6%9E%90-%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-%E7%AC%AC2-3%E8%AC%9B-pandas-%E5%9F%BA%E6%9C%ACfunction%E4%BB%8B%E7%B4%B9-series-dataframe-selection-grouping-447a3fa90b60 https://medium.com/jameslearningnote/%E8%B3%87%E6%96%99%E5%88%86%E6%9E%90-%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-%E7%AC%AC2-4%E8%AC%9B-%E8%B3%87%E6%96%99%E5%89%8D%E8%99%95%E7%90%86-missing-data-one-hot-encoding-feature-scaling-3b70a7839b4a https://medium.com/jameslearningnote/%E8%B3%87%E6%96%99%E5%88%86%E6%9E%90-%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-%E7%AC%AC2-5%E8%AC%9B-%E8%B3%87%E6%96%99%E8%A6%96%E8%A6%BA%E5%8C%96-matplotlib-seaborn-plotly-75cd353d6d3f https://medium.com/jameslearningnote/資料分析-機器學習-第3-1講-python-機器學習以及scikit-learn介紹-fdb052463911 https://medium.com/jameslearningnote/%E8%B3%87%E6%96%99%E5%88%86%E6%9E%90-%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-%E7%AC%AC3-2%E8%AC%9B-%E7%B7%9A%E6%80%A7%E5%88%86%E9%A1%9E-%E6%84%9F%E7%9F%A5%E5%99%A8-perceptron-%E4%BB%8B%E7%B4%B9-84d8b809f866 https://medium.com/jameslearningnote/%E8%B3%87%E6%96%99%E5%88%86%E6%9E%90-%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-%E7%AC%AC3-3%E8%AC%9B-%E7%B7%9A%E6%80%A7%E5%88%86%E9%A1%9E-%E9%82%8F%E8%BC%AF%E6%96%AF%E5%9B%9E%E6%AD%B8-logistic-regression-%E4%BB%8B%E7%B4%B9-a1a5f47017e5 https://medium.com/jameslearningnote/資料分析-機器學習-第3-4講-支援向量機-support-vector-machine-介紹-9c6c6925856b https://medium.com/jameslearningnote/%E8%B3%87%E6%96%99%E5%88%86%E6%9E%90-%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-%E7%AC%AC5-4%E8%AC%9B-%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92%E9%80%B2%E9%9A%8E%E5%AF%A6%E7%94%A8%E6%8A%80%E5%B7%A7-%E6%AD%A3%E8%A6%8F%E5%8C%96-8dd14fcd3140 ## pca https://leemeng.tw/essence-of-principal-component-analysis.html **(推推推)** https://medium.com/d-d-mag/%E6%B7%BA%E8%AB%87%E5%85%A9%E7%A8%AE%E9%99%8D%E7%B6%AD%E6%96%B9%E6%B3%95-pca-%E8%88%87-t-sne-d4254916925b ## 線性代數 https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab --- **(不推)** https://ithelp.ithome.com.tw/users/20107244/ironman/1726 https://ithelp.ithome.com.tw/articles/10204460 https://ithelp.ithome.com.tw/articles/10204845 ## 資料標準化 加速訓練收斂使用 https://ithelp.ithome.com.tw/articles/10197357 **(推)** https://aifreeblog.herokuapp.com/posts/54/data_science_203/ **(推)** https://stats.stackexchange.com/questions/70801/how-to-normalize-data-to-0-1-range **(推)** ## 資料集 http://yhhuang1966.blogspot.com/2018/04/keras-cifar-10.html ## sklearn https://stackoverflow.com/questions/54112307/deprecation-warning-in-scikit-learn 改用SimpleImputer https://stackoverflow.com/questions/30667525/importerror-no-module-named-sklearn-cross-validation 改用model_selection https://blog.csdn.net/qq_40773512/article/details/82662191 https://www.itread01.com/content/1541881152.html fit transform fit_transform函數的差別 https://www.studyai.cn/modules/impute.html 改用SimpleImputer https://zhuanlan.zhihu.com/p/33569866 OneHotEncoder和LabelEncoder的講述(比較好) https://stackoverflow.com/questions/54345667/onehotencoder-categorical-features-depreciated-how-to-transform-specific-column (categorical_features 參數被廢棄!) https://tree.rocks/python/sklearn-explain-onehotencoder-use/ OneHotEncoder https://www.cnblogs.com/zhoukui/p/9159909.html OneHotEncoder https://www.itread01.com/elllp.html OneHotEncoder https://zhengheng.me/2017/08/01/house-pricing/ https://www.cnblogs.com/chaosimple/p/4153167.html 標準化(z分數) > 結果將資料變成平均數0,標準差為1的資料!!! ### 官方文件 https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html https://scikit-learn.org/stable/modules/generated/sklearn.compose.ColumnTransformer.html # Deep learning https://jerrynest.io/deep-learning-resource/ https://zhuanlan.zhihu.com/p/25794795 **(不推)** https://medium.com/%E9%9B%9E%E9%9B%9E%E8%88%87%E5%85%94%E5%85%94%E7%9A%84%E5%B7%A5%E7%A8%8B%E4%B8%96%E7%95%8C/%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-ml-note-overfitting-%E9%81%8E%E5%BA%A6%E5%AD%B8%E7%BF%92-6196902481bb **(推)** https://medium.com/%E9%9B%9E%E9%9B%9E%E8%88%87%E5%85%94%E5%85%94%E7%9A%84%E5%B7%A5%E7%A8%8B%E4%B8%96%E7%95%8C/%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-ml-note-convolution-neural-network-%E5%8D%B7%E7%A9%8D%E7%A5%9E%E7%B6%93%E7%B6%B2%E8%B7%AF-bfa8566744e9 **(中大推)** https://medium.com/%E9%9B%9E%E9%9B%9E%E8%88%87%E5%85%94%E5%85%94%E7%9A%84%E5%B7%A5%E7%A8%8B%E4%B8%96%E7%95%8C/%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-ml-note-cnn%E6%BC%94%E5%8C%96%E5%8F%B2-alexnet-vgg-inception-resnet-keras-coding-668f74879306 **(中大推)** https://www.zhihu.com/question/36686900 **(推)** http://scs.ryerson.ca/~aharley/vis/conv/?source=post_page--------------------------- 模擬lenet的運算 **(推)** --- https://zh.wikipedia.org/wiki/%E9%81%8E%E9%81%A9 --- https://docs.aws.amazon.com/zh_tw/machine-learning/latest/dg/model-fit-underfitting-vs-overfitting.html 過適&乏適 --- **(推推)** https://medium.com/@syshen/%E5%85%A5%E9%96%80%E6%B7%B1%E5%BA%A6%E5%AD%B8%E7%BF%92-1-ed873b65bc5 https://medium.com/@syshen/%E5%85%A5%E9%96%80%E6%B7%B1%E5%BA%A6%E5%AD%B8%E7%BF%92-2-d694cad7d1e5 **寫得好推** https://medium.com/cubo-ai/%E7%89%A9%E9%AB%94%E5%81%B5%E6%B8%AC-object-detection-740096ec4540 **(物體偵測 推推!)** --- https://brohrer.mcknote.com/zh-Hant/ https://brohrer.mcknote.com/zh-Hant/how_machine_learning_works/how_neural_networks_work.html https://brohrer.mcknote.com/zh-Hant/how_machine_learning_works/how_convolutional_neural_networks_work.html **(推)** https://brohrer.mcknote.com/zh-Hant/how_machine_learning_works/how_rnns_lstm_work.html **(推)** --- **(可)** https://ithelp.ithome.com.tw/users/20001976/ironman/1395 https://ithelp.ithome.com.tw/articles/10191404 https://ithelp.ithome.com.tw/articles/10191528 https://ithelp.ithome.com.tw/articles/10191627 https://ithelp.ithome.com.tw/articles/10191725 https://ithelp.ithome.com.tw/articles/10192950 --- **(推)** https://medium.com/jameslearningnote/%E8%B3%87%E6%96%99%E5%88%86%E6%9E%90-%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-%E7%AC%AC5-1%E8%AC%9B-%E5%8D%B7%E7%A9%8D%E7%A5%9E%E7%B6%93%E7%B6%B2%E7%B5%A1%E4%BB%8B%E7%B4%B9-convolutional-neural-network-4f8249d65d4f --- **(推)** https://tomkuo139.blogspot.com/2018/03/ai.html https://tomkuo139.blogspot.com/2018/05/aimlp-cifar10.html https://tomkuo139.blogspot.com/2018/05/aicnn-cifar10.html --- **(推)** https://medium.com/@baubibi https://medium.com/@baubibi/%E9%80%9F%E8%A8%98ai%E8%AA%B2%E7%A8%8B-%E6%B7%B1%E5%BA%A6%E5%AD%B8%E7%BF%92%E5%85%A5%E9%96%80-%E4%B8%80-68e27912ce30 https://medium.com/@baubibi/%E9%80%9F%E8%A8%98ai%E8%AA%B2%E7%A8%8B-convolutional-neural-networks-for-computer-vision-applications-%E4%B8%80-e7059b3e0071 --- https://mropengate.blogspot.com/2017/02/deep-learning-role-of-activation.html 使用激勵函數的原因 https://kknews.cc/zh-tw/tech/y848kvn.html 權重初始化(另一篇) https://zhuanlan.zhihu.com/p/25110150 權重初始化 * sigmoid, tanh => Xavier (別用，反向傳播之下有梯度消失的問題) * relu => He * 減少對初始化的依賴 Batch Norm. --- 改善cnn辨識率 > 內心os:實際訓練會常常發生overfit的狀況!XD https://medium.com/@syshen/改善-cnn-辨識率-dac9fce59b63 **(推)** --- **(推)** https://medium.com/@CinnamonAITaiwan https://medium.com/@CinnamonAITaiwan/cnn%E6%A8%A1%E5%9E%8B-%E6%90%8D%E5%A4%B1%E5%87%BD%E6%95%B8-loss-function-647e13956c50 https://medium.com/datadriveninvestor/l1-l2-regularization-7f1b4fe948f2 https://medium.com/@CinnamonAITaiwan/cnn%E5%85%A5%E9%96%80-overfitting-d10acd15ec21 overfit討厭! https://medium.com/@CinnamonAITaiwan/%E6%B7%B1%E5%BA%A6%E5%AD%B8%E7%BF%92-cnn%E5%8E%9F%E7%90%86-keras%E5%AF%A6%E7%8F%BE-432fd9ea4935 https://medium.com/@CinnamonAITaiwan/cnn%E5%85%A5%E9%96%80-%E5%9C%96%E5%83%8F%E5%A2%9E%E5%BC%B7-fa654d36dafc https://medium.com/@CinnamonAITaiwan/cnn%E6%A8%A1%E5%9E%8B-resnet-mobilenet-densenet-shufflenet-efficientnet-5eba5c8df7e4 --- https://medium.com/@chih.sheng.huang821/%E5%8D%B7%E7%A9%8D%E7%A5%9E%E7%B6%93%E7%B6%B2%E8%B7%AF-convolutional-neural-network-cnn-cnn%E9%81%8B%E7%AE%97%E6%B5%81%E7%A8%8B-ecaec240a631 --- http://elmer-storage.blogspot.com/2018/07/cnn-convolutional-neural-network-cnn.html --- https://medium.com/bryanyang0528/deep-learning-keras-%E6%89%8B%E5%AF%AB%E8%BE%A8%E8%AD%98-mnist-b41757567684 --- **(基本上我覺得還不錯!!!XD)** http://hemingwang.blogspot.com/2016/12/ai_20.html **(推)** http://hemingwang.blogspot.com/2018/09/ai_10.html **(可)** http://hemingwang.blogspot.com/2018/09/ai_50.html **(可)** http://hemingwang.blogspot.com/2017/06/aiweight-decay.html http://hemingwang.blogspot.com/2019/05/trilogy.html ## 圖解dl https://leemeng.tw/deep-learning-for-everyone-understand-neural-net-and-linear-algebra.html https://leemeng.tw/deep-learning-resources.html --- ## 李弘毅 https://violin-tao.blogspot.com/2017/07/ml-introduction-of-deep-learning.html https://violin-tao.blogspot.com/2017/07/ml-backpropagation.html https://violin-tao.blogspot.com/2017/07/ml-tips-for-training-dnn.html https://violin-tao.blogspot.com/2017/07/ml-convolutional-neural-network-cnn.html https://violin-tao.blogspot.com/2017/07/ml-why-deep.html https://violin-tao.blogspot.com/2018/02/ml-batch-normalization.html --- ## keras **(推)** https://machinelearningmastery.com/how-to-reduce-overfitting-in-deep-learning-with-weight-regularization/ https://stackoverflow.com/questions/41260042/global-weight-decay-in-keras https://ithelp.ithome.com.tw/articles/10191725 https://github.com/keras-team/keras/issues/2717 https://ithelp.ithome.com.tw/articles/10191627 https://chtseng.wordpress.com/2017/09/23/%E5%AD%B8%E7%BF%92%E4%BD%BF%E7%94%A8keras%E5%BB%BA%E7%AB%8B%E5%8D%B7%E7%A9%8D%E7%A5%9E%E7%B6%93%E7%B6%B2%E8%B7%AF/ ### 官方文件 https://keras.io/zh/losses/ https://keras.io/zh/metrics/ https://keras.io/zh/optimizers/ https://keras.io/zh/activations/ https://keras.io/zh/datasets/ https://keras.io/zh/initializers/ https://keras.io/zh/regularizers/ L2正規化去懲罰權重 https://keras.io/zh/visualization/ https://keras.io/zh/preprocessing/image/ 循序模型 https://keras.io/zh/getting-started/sequential-model-guide/ https://keras.io/zh/models/sequential/ 函數api(平行模型) https://keras.io/zh/getting-started/functional-api-guide/ https://keras.io/zh/models/model/ Q&A https://keras.io/zh/getting-started/faq 核心網路層 https://keras.io/zh/layers/core/ 卷積層(Conv2D) https://keras.io/zh/layers/convolutional/ 池化層(MaxPooling2D) https://keras.io/zh/layers/pooling/ Batch Norm.層 https://keras.io/zh/layers/normalization/ 資料增強(data augmentation) https://chtseng.wordpress.com/2017/11/11/data-augmentation-資料增強/ https://github.com/ch-tseng/data-augmentation-Keras/blob/master/DataAugmentation-1.ipynb 範例筆記本 cnn padding same stride > 1 https://www.chzzz.club/post/192.html https://blog.csdn.net/wuzqChom/article/details/74785643 --- ## tensorflow 一些colab的線上教學 https://colab.research.google.com/notebooks/mlcc/hello_world.ipynb https://colab.research.google.com/notebooks/mlcc/tensorflow_programming_concepts.ipynb https://colab.research.google.com/notebooks/mlcc/creating_and_manipulating_tensors.ipynb --- ## colab **(推)** 電腦太老的好東西 **記得開gpu訓練 否則你要等很久.......** https://medium.com/pyradise/%E4%B8%8A%E5%82%B3%E6%AA%94%E6%A1%88%E5%88%B0google-colab-dd5369a0bbfd https://medium.com/@white1033/%E5%88%A9%E7%94%A8google-colaboratory-%E4%BD%BF%E7%94%A8%E5%85%8D%E8%B2%BBgpu-b98352b9575d 免費的tpu及gpu https://technews.tw/2017/04/07/first-in-depth-look-at-googles-tpu-architecture/ tpu的好處 https://mattwang44.github.io/en/articles/colab/ 基本操作 https://mc.ai/%E7%AC%AC%E4%B8%80%E6%AC%A1%E7%94%A8-google-colab-%E5%B0%B1%E4%B8%8A%E6%89%8B/ ## 特殊技巧 ### 鐵人賽文章 https://ithelp.ithome.com.tw/articles/10219519 https://ithelp.ithome.com.tw/users/20120243/ironman/2404?page=1 ### 在上面跑yolo(先跪再說) https://stackoverflow.com/questions/54886155/realtime-yolo-object-detection-using-laptop-webcam-in-google-colab https://github.com/AlexeyAB/darknet/issues/2398 https://ithelp.ithome.com.tw/articles/10225527 https://ithelp.ithome.com.tw/articles/10226014 http://blog.ibanyez.info/blogs/coding/20190410-run-a-google-colab-notebook-to-train-yolov3-using-darknet-in/ ### 開啟camera https://stackoverflow.com/questions/58772581/how-to-use-yolov3-on-raspberry-pi-4-with-high-performance https://colab.research.google.com/drive/1tbAeRge6KKgCYdC6ihDrsl80aRYoVOMa ### 裝cuda cudnn https://zhuanlan.zhihu.com/p/54389036 https://colab.research.google.com/drive/14OyDrmxzBmkJ8H51iodPE2aXHzCduKJP#scrollTo=ZvptPv3iqEJ5 --- ## Pytorch https://medium.com/pyladies-taiwan/深度學習新手村-pytorch入門-511df3c1c025 https://zhuanlan.zhihu.com/p/54350088 https://fgc.stpi.narl.org.tw/activity/videoDetail/4b1141305d9cd231015d9d0992ef0030 --- ## Yolo v2 v3 https://zhuanlan.zhihu.com/p/58776542 https://www.zhihu.com/appview/p/45845454 (國人佛心來著!) https://github.com/pjreddie/darknet (原作者佛心來著!) https://github.com/AlexeyAB/darknet (windows下安裝推!) http://yy-programer.blogspot.com/2019/01/yolo-darknet.html (大推安裝好文!) 總之! 不要不信邪! 他要什麼版本的軟體你就照著他去弄! 不要像我一樣! 像個87試了老半天才成功!(淚灑!) https://medium.com/@chih.sheng.huang821/%E6%B7%B1%E5%BA%A6%E5%AD%B8%E7%BF%92-%E7%89%A9%E4%BB%B6%E5%81%B5%E6%B8%ACyolov1-yolov2%E5%92%8Cyolov3-cfg-%E6%AA%94%E8%A7%A3%E8%AE%80-75793cd61a01 https://medium.com/@chih.sheng.huang821/%E6%B7%B1%E5%BA%A6%E5%AD%B8%E7%BF%92-%E7%89%A9%E4%BB%B6%E5%81%B5%E6%B8%ACyolov1-yolov2%E5%92%8Cyolov3-cfg-%E6%AA%94%E8%A7%A3%E8%AE%80-%E4%BA%8C-f5c2347bea68 https://medium.com/@wayne18308/yolo%E5%AF%A6%E4%BD%9C%E6%95%99%E5%AD%B8-train%E5%87%BA%E5%B1%AC%E6%96%BC%E8%87%AA%E5%B7%B1%E7%9A%84model%E4%BD%BF%E7%94%A8darkflow-windows-9b633b7350d8 http://dreamisadream97.pixnet.net/blog/post/172166721-%E5%88%A9%E7%94%A8-yoiov3-%E8%A8%93%E7%B7%B4%E8%87%AA%E5%B7%B1%E7%9A%84%E6%95%B8%E6%93%9A-%EF%BC%88%E8%A9%B3%E7%B4%B0%E6%95%99%E5%AD%B8%EF%BC%89 https://medium.com/%E9%9B%9E%E9%9B%9E%E8%88%87%E5%85%94%E5%85%94%E7%9A%84%E5%B7%A5%E7%A8%8B%E4%B8%96%E7%95%8C/%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-ml-note-yolo-%E5%88%A9%E7%94%A8%E5%BD%B1%E5%83%8F%E8%BE%A8%E8%AD%98%E5%81%9A%E7%89%A9%E4%BB%B6%E5%81%B5%E6%B8%AC-object-detection-%E7%9A%84%E6%8A%80%E8%A1%93-3ad34a4cac70 ** yolov2 ** https://saferauto.home.blog/2019/04/06/4o-how-to-install-yolo-darknet-with-cuda-and-opencv-in-ubuntu/ https://zhuanlan.zhihu.com/p/41230124 ### 某一個大佬的v3教學 https://github.com/inhail/darkflow https://medium.com/@yanweiliu/python%E5%BD%B1%E5%83%8F%E8%BE%A8%E8%AD%98%E7%AD%86%E8%A8%98-%E5%85%AB-%E5%88%86%E5%88%A5%E5%9C%A8windows%E5%92%8Cubuntu-18-04%E4%B8%8A%E5%AE%89%E8%A3%9D%E4%B8%A6%E5%9F%B7%E8%A1%8Cyolov3-%E4%BD%BF%E7%94%A8gpu-d2b77347fde https://medium.com/@yanweiliu/python%E5%BD%B1%E5%83%8F%E8%BE%A8%E8%AD%98%E7%AD%86%E8%A8%98-%E4%B9%9D%E4%B9%8B%E4%BA%8C-%E9%97%9C%E6%96%BCyolov3%E7%9A%84%E4%B8%80%E4%BA%9B%E5%BF%83%E5%BE%97-754b3a7e70e1 https://medium.com/@yanweiliu/python%E5%BD%B1%E5%83%8F%E8%BE%A8%E8%AD%98%E7%AD%86%E8%A8%98-%E4%B9%9D%E4%B9%8B%E4%B8%89-yolov3%E7%B5%90%E5%90%88%E6%B7%B1%E5%BA%A6%E5%AD%B8%E7%BF%92%E7%9A%84object-detector-65440b54422b https://medium.com/@yanweiliu/nvidia-jetson-tx2%E5%AD%B8%E7%BF%92%E7%AD%86%E8%A8%98-%E4%B8%89-%E5%AE%89%E8%A3%9Dopencv-c62e2435ad57 ### labelimg https://tzutalin.github.io/labelImg/ ### darkflow(python版) https://github.com/thtrieu/darkflow ### 樹梅派也要秀yolov3 > ninja 潮他媽的！ > https://medium.com/@yanweiliu/raspberry-pi%E5%AD%B8%E7%BF%92%E7%AD%86%E8%A8%98-%E4%BA%8C%E5%8D%81%E4%B8%83-%E5%9C%A8pi%E4%B8%8A%E5%9F%B7%E8%A1%8Cyolov3-9cf124d5d582 ### Anchor https://github.com/pjreddie/darknet/issues/568 https://stackoverflow.com/questions/54068396/should-i-change-the-value-of-anchors-in-yolo-obj-cfg https://stackoverflow.com/questions/56442413/generating-anchor-boxes-using-k-means-clustering-yolo https://www.angtk.com/article/56 https://github.com/pjreddie/darknet/issues/911 ### Map https://www.zhihu.com/question/53405779 https://blog.csdn.net/hysteric314/article/details/54093734 --- ## 技巧 https://ithelp.ithome.com.tw/articles/10190971 遷移學習 ## 作者大神論文 https://hackmd.io/@allen108108/r1-wSTAjS **好心人** --- ## 學習地圖 **技能樹:** 1. 微積分的微分 2. 統計 3. 線性代數(矩陣、向量的操作) 4. Python(**Numpy、matplotlib.pyplot、pandas、opencv**、tensorflow、keras、sklearn) **機器學習(ML)範圍:** * 感知器(perceptron) => NN網路(affine, 全連接) **深度學習(DL)範圍:** **影像方面:** * 接續 => CNN(小難) => 深度學習 **文字方面:** * 接續 => RNN => LSTM => 深度學習 # python ## pickle 序列化 反序列化 把物件存成檔案!XD https://morvanzhou.github.io/tutorials/python-basic/basic/13-08-pickle/ ## PIL的image與numpy矩陣互相轉換 https://www.jianshu.com/p/18dabefa6778 ## **numpy** (必學！矩陣運算包) **(推)** https://www.twblogs.net/a/5b8fccbb2b71776722159c6f https://blog.csdn.net/baoqian1993/article/details/52116164 ### numpy axis概念整理 http://changtw-blog.logdown.com/posts/895468-python-numpy-axis-concept-organize-notes ## pandas (似excel) **(推)** https://medium.com/@weilihmen/python-pandas-%E5%9F%BA%E6%9C%AC%E6%93%8D%E4%BD%9C%E6%95%99%E5%AD%B8-%E6%88%90%E7%B8%BE%E8%A1%A8-f6d0ec4f89 https://colab.research.google.com/notebooks/mlcc/intro_to_pandas.ipynb colab有關pandas的入門教學 https://blog.csdn.net/qq1483661204/article/details/77587881 iloc用法 ## Matplotlib **(推)** https://www.itread01.com/content/1542579970.html ## numpy pandas matplotlib (畫圖包) https://codertw.com/%E7%A8%8B%E5%BC%8F%E8%AA%9E%E8%A8%80/565247/ ## opencv (影像處理包) **(小推，做cv時，用得到)** https://blog.gtwang.org/programming/opencv-basic-image-read-and-write-tutorial/ 基本讀取操作 http://jennaweng0621.pixnet.net/blog/post/403254017-opencv-%E5%BD%A9%E8%89%B2%E8%BD%89%E7%81%B0%E9%9A%8E%28rgb-to-gray%29 https://blog.csdn.net/on2way/article/details/46812121 反相黑白 https://www.jianshu.com/p/3977d674da85 http://monkeycoding.com/?page_id=12 # 軟體 ## anaconda 相關 (python資料科學懶人包) **(推)** https://medium.com/python4u/%E7%94%A8conda%E5%BB%BA%E7%AB%8B%E5%8F%8A%E7%AE%A1%E7%90%86python%E8%99%9B%E6%93%AC%E7%92%B0%E5%A2%83-b61fd2a76566 https://tomkuo139.blogspot.com/2018/03/anaconda-tensorflow-keras.html 安裝tensorflow keras ## jupyter notebook **(推)** http://opus.konghy.cn/ipynb/jupyter-notebook-keyboard-shortcut.html # (可能)參考書籍 https://www.books.com.tw/products/0010822932 (o) **推推推** https://www.books.com.tw/products/0010761759 (o) **(大大大大大推)** https://www.books.com.tw/products/0010817138 (x) https://www.books.com.tw/products/0010754327?sloc=reprod_i_10 (x) http://tensorflowkeras.blogspot.com/2017/08/keras.html (x) https://www.books.com.tw/products/0010822845?sloc=reprod_i_11 (o) **不太推** https://www.books.com.tw/products/0010811811 (x, 評價...恩) https://www.books.com.tw/products/0010797283?loc=P_asb_002 (o) ....(待增加) # 課程 https://zh-tw.coursera.org/learn/machine-learning 史丹佛大學 吳恩達教授 https://www.youtube.com/channel/UC2ggjtuuWvxrHHHiaDH1dlQ 台大 李宏毅教授 # 統計相關 https://www.idomaths.com/zh-Hant/probability5.php 貝式定理 https://medium.com/@c824751/confusion-matrix-%E6%B7%B7%E6%B7%86%E7%9F%A9%E9%99%A3-f6ddf6e6aa58 混淆矩陣 https://medium.com/@chih.sheng.huang821/機器學習-統計方法-模型評估-驗證指標-b03825ff0814 https://blog.csdn.net/hjxu2016/article/details/72817374 機器學習之中的混淆矩陣 https://kknews.cc/zh-tw/news/ma2g9n9.html 甚麼是tensor(張量) 似然函數及最大似然估計法(經二項分布) https://pansci.asia/archives/124043 > 由結果去求原始的機率 > e.g. 取10顆球有1顆紅球的機率為0.1 > 其實就是代表「取10顆球有1顆紅球的機率「最有」可能是10%，可能還是有誤差!!! > https://kknews.cc/zh-tw/game/j6vgbnl.html **(最大似然估計法)** > > 而極大似然法就是令樣本出現的機率最大，進而估計整體的模型參數。 > (最符合這個(觀察出來的)機率分佈的模型) http://ccckmit.wikidot.com/st:maximumlikelihood **(最大似然估計法)** > >因此，設定 p(x)=p′(x) 的想法，其背後的目標乃是要最大化機率原模型 p 產生 p' 現象的可能性，這個最大化的目標就稱為最大似然法則。
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