File size: 4,537 Bytes
be0e064 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | 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
|