/ML-DL

CS231n + 잡다한 Deep Learning 기초 지식

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Standford CS231n Summary

After watching all the videos of the famous Standford's CS231n course that took place in 2021, I decided to summarize the entire lecture for those who want to remember the content and learn more about it.

cs231n assignments

assignments Contents Date check
assignment_1 Knn 2021-07-10 ✅
assignment_1 SVM, Softmax 2021-07-11 ✅
assignment_1 Two_layer_net, Features 2021-07-13 ✅
assignment_2 FullyConnectedNets 2021-07-26 ✅
assignment_2 BatchNormalization 2021-07-26 ✅
assignment_1 Dropout 2021-07-27 ✅
assignment_1 ConvolutionalNetworks 2021-07-29 ✅
assignment_2 PyTorch, TensorFlow 2021-07-30 ✅
assignment_3 RNN_Captioning 2021-08-03 ✅
assignment_3 LSTM_Captioning 2021-08-04 ✅
assignment_3 Transformer_Captioning 2021-08-11 ✅
assignment_3 Generative_Adversarial_Networks 2021-08-08 ✅
assignment_3 Network_Visualization 2021-08-10 ✅
assignment_3 Self_Supervised_Learning 2021-08-12 ✅

Any questions please email to Ⓜ️ a01152a@gmail.com

Explanation here 🔗 RoadoneP Velog 😄

EDA & FE

Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to

  • Kaggle-titanic

    Kaggle 데이터 셋인 Titanic Machine Learning From Disaster의 EDA & FE 튜토리얼 입니다. 😊

  • Heart disease

    Kaggle 데이터 셋인 Heart Disease의 EDA & FE튜토리얼 입니다.

ML (Using Skitlearn)

  • DecisionTree, RandomForest

    • It is a supervised learning method used for classification and regression.
    • Predict values ​​through decision rules inferred from data characteristics.
    • Learning data through if-then-else decision rules.
    • The deeper the tree, the more complex the model.
    • Advantages of decision trees Easy to understand and interpret.
    • It does not require much data preprocessing.
    • It can handle both numeric and categorical data.

  • SVM

    • Supervised learning methods used for regression, classification, and outlier detection
    • Data points located on the boundary between classes are called support vectors.
    • Learn how important each support vector is in demarcating decision boundaries between classes.