| parent,child |
| Machine Learning and Data Analytics,Machine Learning |
| Machine Learning and Data Analytics,Deep Learning |
| Machine Learning,Algorithms of Machine Learning |
| Machine Learning,Supervised Learning |
| Machine Learning,Unsupervised Learning |
| Supervised Learning,Classification |
| Supervised Learning,Regression |
| Unsupervised Learning,Clustering |
| Unsupervised Learning,Semi-sup Learning |
| Unsupervised Learning,Generative Models |
| Algorithms of Machine Learning,Parametric Algorithm |
| Algorithms of Machine Learning,Non-parametric Algorithms |
| Non-parametric Algorithms,K-Nearest Neighbors (KNN) |
| K-Nearest Neighbors (KNN),Classification |
| K-Nearest Neighbors (KNN),Regression |
| Regression,Linear Regression |
| Linear Regression,Loss Function |
| Linear Regression,Gradient Descent |
| Linear Regression,Variance and Bias |
| Supervised Learning,Data Spilit |
| Data Spilit,Training Data |
| Data Spilit,Testing Data |
| Data Spilit,Validation Data |
| Regression,Logistic Regression |
| Logistic Regression,Classification |
| Classification,Naïve Bayes Classifier |
| Logistic Regression,Log-likelihood |
| Classification,Cross Entropy Loss |
| Logistic Regression,Advantages of Logistic Regression |
| Advantages of Logistic Regression,Better Predictive Accuracy |
| Advantages of Logistic Regression,Handle Feature Preprocessing |
| Advantages of Logistic Regression,Well-calibrated Probabilities |
| Naïve Bayes Classifier,Advantages of NBC |
| Advantages of NBC,Easy to Fit |
| Advantages of NBC,Handle Missing Input Features |
| Advantages of NBC,Fit Classes Separately |
| Naïve Bayes Classifier,Handle Unlabelled Training Data |
| Deep Learning,Deep Neural Network |
| Deep Neural Network,Linear Basics of Neural Network |
| Linear Basics of Neural Network,Linear Regression |
| Linear Basics of Neural Network,Back Propagation |
| Back Propagation,Gradient Descent |
| Back Propagation,Loss Function |
| Deep Learning,Evaluation of Deep Learning |
| Evaluation of Deep Learning,Overfitting |
| Evaluation of Deep Learning,Underfitting |
| Underfitting,Gradient vanishing |
| Overfitting,Regularization |
| Gradient Descent,Batch Gradient Descent |
| Gradient Descent,Stochastic Gradient Descent |
| Gradient vanishing,Sigmoid Function |
| Gradient vanishing,RELU function |
| Regularization,Data augmentation |
| Regularization,Early stopping |
| Regularization,Dropout |
| Deep Learning,Convolutional Neural Networks |
| Convolutional Neural Networks,Convolutional Neural Layer |
| Convolutional Neural Layer,kernel / filter / pattern |
| Convolutional Neural Layer,Image Features |
| Convolutional Neural Layer,Max Pooling |
| Convolutional Neural Layer,Padding |
| Deep Learning,Recurrent Neural Networks (RNNs) |
| Recurrent Neural Networks (RNNs),LSTM |
| LSTM,Cell State |
| LSTM,Hidden State |
| Recurrent Neural Networks (RNNs),Sequence to Sequence Model |
| Sequence to Sequence Model,Encoder |
| Sequence to Sequence Model,Decoder |
| Sequence to Sequence Model,LSTM |
| Recurrent Neural Networks (RNNs),Attention Layer |
| Attention Layer,Transformer Structure |
| Attention Layer,Self-Attention |
| Transformer Structure,Self-Attention |
| Transformer Structure,Query |
| Transformer Structure,Key |
| Transformer Structure,Value |
| Self-Attention,Multi-head Attention |
| Deep Learning,Graph Neural Network |
| Graph Neural Network,Problem Formulation of GNN |
| Problem Formulation of GNN,Link/Edge Prediction |
| Problem Formulation of GNN,Node Classification |
| Problem Formulation of GNN,Graph Classification |
| Graph Neural Network,Graph Types |
| Graph Types,Heterogeneous Graph |
| Graph Types,Dynamic Graph |
| Graph Types,Directed Graph |
| Graph Types,Edge-informative Graph |
| Graph Neural Network,Graph Convolution Network |
| Graph Convolution Network,Graph Pooling |
| Unsupervised Learning,Dimension Reduction |
| Generative Models,Density Estimation |
| Clustering,K-means |
| Unsupervised Learning,Self-supervised Learning |
| Dimension Reduction,Autoencoder |
| Autoencoder,Applications of Autoencoders |
| Applications of Autoencoders,Recommender System |
| Applications of Autoencoders,Image Compression |
| Applications of Autoencoders,Clustering |
| Applications of Autoencoders,Dimension Reduction |
| Dimension Reduction,Capsule Neural Layer |
| Unsupervised Learning,Variational Autoencoder |
| Unsupervised Learning,Generative Adversarial Network |
| Variational Autoencoder,Reparameterization Trick |
| Generative Adversarial Network,Discriminator |
| Generative Adversarial Network,Generator |
| Self-supervised Learning,Contrastive Learning |
| Contrastive Learning,Contrastive Loss |
| Contrastive Learning,Triplet Loss |
| Machine Learning,Reinforcement Learning |
| Reinforcement Learning,Contrastive Learning |
| Reinforcement Learning,Supervised Learning |
| Reinforcement Learning,Policy Gradient Methods |
| Reinforcement Learning,Environment |
| Environment,Reward |
| Environment,State |
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