This article is reprinted from the public account Brother Biao has something to say
Source: Internet
Below is a comprehensive glossary of 725 machine learning terms compiled by several authoritative experts in machine learning, which is very comprehensive and worth collecting!
| English Term | Chinese Translation |
|---|---|
| 0-1 Loss Function | 0-1 Loss Function |
| Accept-Reject Sampling Method | Accept-Reject Sampling Method |
| Accumulated Error Backpropagation | Accumulated Error Backpropagation |
| Accuracy | Accuracy |
| Acquisition Function | Acquisition Function |
| Action | Action |
| Activation Function | Activation Function |
| Active Learning | Active Learning |
| Adaptive Bitrate Algorithm | Adaptive Bitrate Algorithm |
| Adaptive Boosting | AdaBoost |
| Adaptive Gradient Algorithm | AdaGrad |
| Adaptive Moment Estimation Algorithm | Adam Algorithm |
| Adaptive Resonance Theory | Adaptive Resonance Theory |
| Additive Model | Additive Model |
| Affinity Matrix | Affinity Matrix |
| Agent | Agent |
| Algorithm | Algorithm |
| Alpha-Beta Pruning | Alpha-Beta Pruning |
| Anomaly Detection | Anomaly Detection |
| Approximate Inference | Approximate Inference |
| Area Under ROC Curve | AUC |
| Artificial Intelligence | Artificial Intelligence |
| Artificial Neural Network | Artificial Neural Network |
| Artificial Neuron | Artificial Neuron |
| Attention | Attention |
| Attention Mechanism | Attention Mechanism |
| Attribute | Attribute |
| Attribute Space | Attribute Space |
| Autoencoder | Autoencoder |
| Automatic Differentiation | Automatic Differentiation |
| Autoregressive Model | Autoregressive Model |
| Back Propagation | Back Propagation |
| Back Propagation Algorithm | Back Propagation Algorithm |
| Back Propagation Through Time | Back Propagation Through Time |
| Backward Induction | Backward Induction |
| Backward Search | Backward Search |
| Bag of Words | Bag of Words |
| Bandit | Bandit |
| Base Learner | Base Learner |
| Base Learning Algorithm | Base Learning Algorithm |
| Baseline | Baseline |
| Batch | Batch |
| Batch Normalization | Batch Normalization |
| Bayes Decision Rule | Bayes Decision Rule |
| Bayes Model Averaging | Bayes Model Averaging |
| Bayes Optimal Classifier | Bayes Optimal Classifier |
| Bayes’ Theorem | Bayes’ Theorem |
| Bayesian Decision Theory | Bayesian Decision Theory |
| Bayesian Inference | Bayesian Inference |
| Bayesian Learning | Bayesian Learning |
| Bayesian Network | Bayesian Network |
| Bayesian Optimization | Bayesian Optimization |
| Beam Search | Beam Search |
| Benchmark | Benchmark |
| Belief Network | Belief Network |
| Belief Propagation | Belief Propagation |
| Bellman Equation | Bellman Equation |
| Bernoulli Distribution | Bernoulli Distribution |
| Beta Distribution | Beta Distribution |
| Between-Class Scatter Matrix | Between-Class Scatter Matrix |
| BFGS | BFGS |
| Bias | Bias |
| Bias In Affine Function | Bias In Affine Function |
| Bias In Statistics | Bias In Statistics |
| Bias Shift | Bias Shift |
| Bias-Variance Decomposition | Bias-Variance Decomposition |
| Bias-Variance Dilemma | Bias-Variance Dilemma |
| Bidirectional Recurrent Neural Network | Bidirectional Recurrent Neural Network |
| Bigram | Bigram |
| Bilingual Evaluation Understudy | BLEU |
| Binary Classification | Binary Classification |
| Binomial Distribution | Binomial Distribution |
| Binomial Test | Binomial Test |
| Boltzmann Distribution | Boltzmann Distribution |
| Boltzmann Machine | Boltzmann Machine |
| Boosting | Boosting |
| Bootstrap Aggregating | Bagging |
| Bootstrap Sampling | Bootstrap Sampling |
| Bootstrapping | Bootstrapping |
| Break-Event Point | Break-Event Point |
| Bucketing | Bucketing |
| Calculus of Variations | Calculus of Variations |
| Cascade-Correlation | Cascade-Correlation |
| Catastrophic Forgetting | Catastrophic Forgetting |
| Categorical Distribution | Categorical Distribution |
| Cell | Cell |
| Chain Rule | Chain Rule |
| Chebyshev Distance | Chebyshev Distance |
| Class | Class |
| Class-Imbalance | Class-Imbalance |
| Classification | Classification |
| Classification And Regression Tree | Classification And Regression Tree |
| Classifier | Classifier |
| Clique | Clique |
| Cluster | Cluster |
| Cluster Assumption | Cluster Assumption |
| Clustering | Clustering |
| Clustering Ensemble | Clustering Ensemble |
| Co-Training | Co-Training |
| Coding Matrix | Coding Matrix |
| Collaborative Filtering | Collaborative Filtering |
| Competitive Learning | Competitive Learning |
| Comprehensibility | Comprehensibility |
| Computation Graph | Computation Graph |
| Computational Learning Theory | Computational Learning Theory |
| Conditional Entropy | Conditional Entropy |
| Conditional Probability | Conditional Probability |
| Conditional Probability Distribution | Conditional Probability Distribution |
| Conditional Random Field | Conditional Random Field |
| Conditional Risk | Conditional Risk |
| Confidence | Confidence |
| Confusion Matrix | Confusion Matrix |
| Conjugate Distribution | Conjugate Distribution |
| Connection Weight | Connection Weight |
| Connectionism | Connectionism |
| Consistency | Consistency |
| Constrained Optimization | Constrained Optimization |
| Context Variable | Context Variable |
| Context Vector | Context Vector |
| Context Window | Context Window |
| Context Word | Context Word |
| Contextual Bandit | Contextual Bandit |
| Contingency Table | Contingency Table |
| Continuous Attribute | Continuous Attribute |
| Contrastive Divergence | Contrastive Divergence |
| Convergence | Convergence |
| Convex Optimization | Convex Optimization |
| Convex Quadratic Programming | Convex Quadratic Programming |
| Convolution | Convolution |
| Convolutional Kernel | Convolutional Kernel |
| Convolutional Neural Network | Convolutional Neural Network |
| Coordinate Descent | Coordinate Descent |
| Corpus | Corpus |
| Correlation Coefficient | Correlation Coefficient |
| Cosine Similarity | Cosine Similarity |
| Cost | Cost |
| Cost Curve | Cost Curve |
| Cost Function | Cost Function |
| Cost Matrix | Cost Matrix |
| Cost-Sensitive | Cost-Sensitive |
| Covariance | Covariance |
| Covariance Matrix | Covariance Matrix |
| Critical Point | Critical Point |
| Cross Entropy | Cross Entropy |
| Cross Validation | Cross Validation |
| Curse of Dimensionality | Curse of Dimensionality |
| Cutting Plane Algorithm | Cutting Plane Algorithm |
| Data Mining | Data Mining |
| Data Set | Data Set |
| Davidon-Fletcher-Powell | DFP |
| Decision Boundary | Decision Boundary |
| Decision Function | Decision Function |
| Decision Stump | Decision Stump |
| Decision Tree | Decision Tree |
| Decoder | Decoder |
| Decoding | Decoding |
| Deconvolution | Deconvolution |
| Deconvolutional Network | Deconvolutional Network |
| Deduction | Deduction |
| Deep Belief Network | Deep Belief Network |
| Deep Boltzmann Machine | Deep Boltzmann Machine |
| Deep Convolutional Generative Adversarial Network | Deep Convolutional Generative Adversarial Network |
| Deep Learning | Deep Learning |
| Deep Neural Network | Deep Neural Network |
| Deep Q-Network | Deep Q-Network |
| Delta-Bar-Delta | Delta-Bar-Delta |
| Denoising | Denoising |
| Denoising Autoencoder | Denoising Autoencoder |
| Denoising Score Matching | Denoising Score Matching |
| Density Estimation | Density Estimation |
| Density-Based Clustering | Density-Based Clustering |
| Derivative | Derivative |
| Determinant | Determinant |
| Diagonal Matrix | Diagonal Matrix |
| Dictionary Learning | Dictionary Learning |
| Dimension Reduction | Dimension Reduction |
| Directed Edge | Directed Edge |
| Directed Graphical Model | Directed Graphical Model |
| Directed Separation | Directed Separation |
| Dirichlet Distribution | Dirichlet Distribution |
| Discriminative Model | Discriminative Model |
| Discriminator | Discriminator |
| Discriminator Network | Discriminator Network |
| Distance Measure | Distance Measure |
| Distance Metric Learning | Distance Metric Learning |
| Distributed Representation | Distributed Representation |
| Diverge | Diverge |
| Divergence | Divergence |
| Diversity | Diversity |
| Diversity Measure | Diversity Measure |
| Domain Adaptation | Domain Adaptation |
| Dominant Strategy | Dominant Strategy |
| Down Sampling | Down Sampling |
| Dropout | Dropout |
| Dropout Boosting | Dropout Boosting |
| Dropout Method | Dropout Method |
| Dual Problem | Dual Problem |
| Dummy Node | Dummy Node |
| Dynamic Bayesian Network | Dynamic Bayesian Network |
| Dynamic Programming | Dynamic Programming |
| Early Stopping | Early Stopping |
| Eigendecomposition | Eigendecomposition |
| Eigenvalue | Eigenvalue |
| Element-Wise Product | Element-Wise Product |
| Embedding | Embedding |
| Empirical Conditional Entropy | Empirical Conditional Entropy |
| Empirical Distribution | Empirical Distribution |
| Empirical Entropy | Empirical Entropy |
| Empirical Error | Empirical Error |
| Empirical Risk | Empirical Risk |
| Empirical Risk Minimization | Empirical Risk Minimization |
| Encoder | Encoder |
| Encoding | Encoding |
| End-To-End | End-To-End |
| Energy Function | Energy Function |
| Energy-Based Model | Energy-Based Model |
| Ensemble Learning | Ensemble Learning |
| Ensemble Pruning | Ensemble Pruning |
| Entropy | Entropy |
| Episode | Episode |
| Epoch | Epoch |
| Error | Error |
| Error Backpropagation Algorithm | Error Backpropagation Algorithm |
| Error Backpropagation | Error Backpropagation |
| Error Correcting Output Codes | Error Correcting Output Codes |
| Error Rate | Error Rate |
| Error-Ambiguity Decomposition | Error-Ambiguity Decomposition |
| Estimator | Estimator |
| Euclidean Distance | Euclidean Distance |
| Evidence | Evidence |
| Evidence Lower Bound | Evidence Lower Bound |
| Exact Inference | Exact Inference |
| Example | Example |
| Expectation | Expectation |
| Expectation Maximization | Expectation Maximization |
| Expected Loss | Expected Loss |
| Expert System | Expert System |
| Exploding Gradient | Exploding Gradient |
| Exponential Loss Function | Exponential Loss Function |
| Factor | Factor |
| Factorization | Factorization |
| Feature | Feature |
| Feature Engineering | Feature Engineering |
| Feature Map | Feature Map |
| Feature Selection | Feature Selection |
| Feature Vector | Feature Vector |
| Featured Learning | Featured Learning |
| Feedforward | Feedforward |
| Feedforward Neural Network | Feedforward Neural Network |
| Few-Shot Learning | Few-Shot Learning |
| Filter | Filter |
| Fine-Tuning | Fine-Tuning |
| Fluctuation | Fluctuation |
| Forget Gate | Forget Gate |
| Forward Propagation | Forward Propagation |
| Forward Stagewise Algorithm | Forward Stagewise Algorithm |
| Fractionally Strided Convolution | Fractionally Strided Convolution |
| Frobenius Norm | Frobenius Norm |
| Full Padding | Full Padding |
| Functional | Functional |
| Functional Neuron | Functional Neuron |
| Gated Recurrent Unit | Gated Recurrent Unit |
| Gated RNN | Gated RNN |
| Gaussian Distribution | Gaussian Distribution |
| Gaussian Kernel | Gaussian Kernel |
| Gaussian Kernel Function | Gaussian Kernel Function |
| Gaussian Mixture Model | Gaussian Mixture Model |
| Gaussian Process | Gaussian Process |
| Generalization Ability | Generalization Ability |
| Generalization Error | Generalization Error |
| Generalization Error Bound | Generalization Error Bound |
| Generalize | Generalize |
| Generalized Lagrange Function | Generalized Lagrange Function |
| Generalized Linear Model | Generalized Linear Model |
| Generalized Rayleigh Quotient | Generalized Rayleigh Quotient |
| Generative Adversarial Network | Generative Adversarial Network |
| Generative Model | Generative Model |
| Generator | Generator |
| Generator Network | Generator Network |
| Genetic Algorithm | Genetic Algorithm |
| Gibbs Distribution | Gibbs Distribution |
| Gibbs Sampling | Gibbs Sampling |
| Gini Index | Gini Index |
| Global Markov Property | Global Markov Property |
| Global Minimum | Global Minimum |
| Gradient | Gradient |
| Gradient Clipping | Gradient Clipping |
| Gradient Descent | Gradient Descent |
| Gradient Descent Method | Gradient Descent Method |
| Gradient Exploding Problem | Gradient Exploding Problem |
| Gram Matrix | Gram Matrix |
| Graph Convolutional Network | Graph Convolutional Network |
| Graph Neural Network | Graph Neural Network |
| Graphical Model | Graphical Model |
| Grid Search | Grid Search |
| Ground Truth | Ground Truth |
| Hadamard Product | Hadamard Product |
| Hamming Distance | Hamming Distance |
| Hard Margin | Hard Margin |
| Hebbian Rule | Hebbian Rule |
| Hidden Layer | Hidden Layer |
| Hidden Markov Model | Hidden Markov Model |
| Hidden Variable | Hidden Variable |
| Hierarchical Clustering | Hierarchical Clustering |
| Hilbert Space | Hilbert Space |
| Hinge Loss Function | Hinge Loss Function |
| Hold-Out | Hold-Out |
| Hyperparameter | Hyperparameter |
| Hyperparameter Optimization | Hyperparameter Optimization |
| Hypothesis | Hypothesis |
| Hypothesis Space | Hypothesis Space |
| Hypothesis Test | Hypothesis Test |
| Identity Matrix | Identity Matrix |
| Imitation Learning | Imitation Learning |
| Importance Sampling | Importance Sampling |
| Improved Iterative Scaling | Improved Iterative Scaling |
| Incremental Learning | Incremental Learning |
| Independent and Identically Distributed | Independent and Identically Distributed |
| Indicator Function | Indicator Function |
| Individual Learner | Individual Learner |
| Induction | Induction |
| Inductive Bias | Inductive Bias |
| Inductive Learning | Inductive Learning |
| Inductive Logic Programming | Inductive Logic Programming |
| Inference | Inference |
| Information Entropy | Information Entropy |
| Information Gain | Information Gain |
| Inner Product | Inner Product |
| Instance | Instance |
| Internal Covariate Shift | Internal Covariate Shift |
| Inverse Matrix | Inverse Matrix |
| Inverse Resolution | Inverse Resolution |
| Isometric Mapping | Isometric Mapping |
| Jacobian Matrix | Jacobian Matrix |
| Jensen Inequality | Jensen Inequality |
| Joint Probability Distribution | Joint Probability Distribution |
| K-Armed Bandit Problem | K-Armed Bandit Problem |
| K-Fold Cross Validation | K-Fold Cross Validation |
| Karush-Kuhn-Tucker Condition | Karush-Kuhn-Tucker Condition |
| Kernel Function | Kernel Function |
| Kernel Method | Kernel Method |
| Kernel Trick | Kernel Trick |
| Kernelized Linear Discriminant Analysis | Kernelized Linear Discriminant Analysis |
| KL Divergence | KL Divergence |
| L-BFGS | L-BFGS |
| Label | Label |
| Label Space | Label Space |
| Lagrange Duality | Lagrange Duality |
| Lagrange Multiplier | Lagrange Multiplier |
| Language Model | Language Model |
| Laplace Smoothing | Laplace Smoothing |
| Laplacian Correction | Laplacian Correction |
| Latent Dirichlet Allocation | Latent Dirichlet Allocation |
| Latent Semantic Analysis | Latent Semantic Analysis |
| Latent Variable | Latent Variable |
| Law of Large Numbers | Law of Large Numbers |
| Layer Normalization | Layer Normalization |
| Lazy Learning | Lazy Learning |
| Leaky Relu | Leaky Relu |
| Learner | Learner |
| Learning | Learning |
| Learning By Analogy | Learning By Analogy |
| Learning Rate | Learning Rate |
| Learning Vector Quantization | Learning Vector Quantization |
| Least Square Method | Least Square Method |
| Least Squares Regression Tree | Least Squares Regression Tree |
| Left Singular Vector | Left Singular Vector |
| Likelihood | Likelihood |
| Linear Chain Conditional Random Field | Linear Chain Conditional Random Field |
| Linear Classification Model | Linear Classification Model |
| Linear Classifier | Linear Classifier |
| Linear Dependence | Linear Dependence |
| Linear Discriminant Analysis | Linear Discriminant Analysis |
| Linear Model | Linear Model |
| Linear Regression | Linear Regression |
| Link Function | Link Function |
| Local Markov Property | Local Markov Property |
| Local Minima | Local Minima |
| Local Minimum | Local Minimum |
| Local Representation | Local Representation |
| Log Likelihood | Log Likelihood |
| Log Linear Model | Log Linear Model |
| Log-Likelihood | Log-Likelihood |
| Log-Linear Regression | Log-Linear Regression |
| Logistic Function | Logistic Function |
| Logistic Regression | Logistic Regression |
| Logit | Logit |
| Long Short Term Memory | Long Short Term Memory |
| Long Short-Term Memory Network | Long Short-Term Memory Network |
| Loopy Belief Propagation | Loopy Belief Propagation |
| Loss Function | Loss Function |
| Low Rank Matrix Approximation | Low Rank Matrix Approximation |
| Machine Learning | Machine Learning |
| Macron-R | Macron-R |
| Manhattan Distance | Manhattan Distance |
| Manifold | Manifold |
| Manifold Assumption | Manifold Assumption |
| Manifold Learning | Manifold Learning |
| Margin | Margin |
| Marginal Distribution | Marginal Distribution |
| Marginal Independence | Marginal Independence |
| Marginalization | Marginalization |
| Markov Chain | Markov Chain |
| Markov Chain Monte Carlo | Markov Chain Monte Carlo |
| Markov Decision Process | Markov Decision Process |
| Markov Network | Markov Network |
| Markov Process | Markov Process |
| Markov Random Field | Markov Random Field |
| Mask | Mask |
| Matrix | Matrix |
| Matrix Inversion | Matrix Inversion |
| Max Pooling | Max Pooling |
| Maximal Clique | Maximal Clique |
| Maximum Entropy Model | Maximum Entropy Model |
| Maximum Likelihood Estimation | Maximum Likelihood Estimation |
| Maximum Margin | Maximum Margin |
| Mean Filed | Mean Filed |
| Mean Pooling | Mean Pooling |
| Mean Squared Error | Mean Squared Error |
| Mean-Field | Mean-Field |
| Memory Network | Memory Network |
| Message Passing | Message Passing |
| Metric Learning | Metric Learning |
| Micro-R | Micro-R |
| Minibatch | Minibatch |
| Minimal Description Length | Minimal Description Length |
| Minimax Game | Minimax Game |
| Minkowski Distance | Minkowski Distance |
| Mixture of Experts | Mixture of Experts |
| Mixture-of-Gaussian | Mixture-of-Gaussian |
| Model | Model |
| Model Selection | Model Selection |
| Momentum Method | Momentum Method |
| Monte Carlo Method | Monte Carlo Method |
| Moral Graph | Moral Graph |
| Moralization | Moralization |
| Multi-Class Classification | Multi-Class Classification |
| Multi-Head Attention | Multi-Head Attention |
| Multi-Head Self-Attention | Multi-Head Self-Attention |
| Multi-Kernel Learning | Multi-Kernel Learning |
| Multi-Label Learning | Multi-Label Learning |
| Multi-Layer Feedforward Neural Networks | Multi-Layer Feedforward Neural Networks |
| Multi-Layer Perceptron | Multi-Layer Perceptron |
| Multinomial Distribution | Multinomial Distribution |
| Multiple Dimensional Scaling | Multiple Dimensional Scaling |
| Multiple Linear Regression | Multiple Linear Regression |
| Multitask Learning | Multitask Learning |
| Multivariate Normal Distribution | Multivariate Normal Distribution |
| Mutual Information | Mutual Information |
| N-Gram Model | N-Gram Model |
| Naive Bayes Classifier | Naive Bayes Classifier |
| Naive Bayes | Naive Bayes |
| Nearest Neighbor Classifier | Nearest Neighbor Classifier |
| Negative Log Likelihood | Negative Log Likelihood |
| Neighbourhood Component Analysis | Neighbourhood Component Analysis |
| Net Input | Net Input |
| Neural Network | Neural Network |
| Neural Turing Machine | Neural Turing Machine |
| Neuron | Neuron |
| Newton Method | Newton Method |
| No Free Lunch Theorem | No Free Lunch Theorem |
| Noise-Contrastive Estimation | Noise-Contrastive Estimation |
| Nominal Attribute | Nominal Attribute |
| Non-Convex Optimization | Non-Convex Optimization |
| Non-Metric Distance | Non-Metric Distance |
| Non-Negative Matrix Factorization | Non-Negative Matrix Factorization |
| Non-Ordinal Attribute | Non-Ordinal Attribute |
| Norm | Norm |
| Normal Distribution | Normal Distribution |
| Normalization | Normalization |
| Nuclear Norm | Nuclear Norm |
| Number of Epochs | Number of Epochs |
| Numerical Attribute | Numerical Attribute |
| Object Detection | Object Detection |
| Oblique Decision Tree | Oblique Decision Tree |
| Occam’s Razor | Occam’s Razor |
| Odds | Odds |
| Off-Policy | Off-Policy |
| On-Policy | On-Policy |
| One-Dependent Estimator | One-Dependent Estimator |
| One-Hot | One-Hot |
| Online Learning | Online Learning |
| Optimizer | Optimizer |
| Ordinal Attribute | Ordinal Attribute |
| Orthogonal | Orthogonal |
| Orthogonal Matrix | Orthogonal Matrix |
| Out-Of-Bag Estimate | Out-Of-Bag Estimate |
| Outlier | Outlier |
| Over-Parameterized | Over-Parameterized |
| Overfitting | Overfitting |
| Oversampling | Oversampling |
| Pac-Learnable | Pac-Learnable |
| Padding | Padding |
| Pairwise Markov Property | Pairwise Markov Property |
| Parallel Distributed Processing | Parallel Distributed Processing |
| Parameter | Parameter |
| Parameter Estimation | Parameter Estimation |
| Parameter Space | Parameter Space |
| Parameter Tuning | Parameter Tuning |
| Parametric ReLU | Parametric ReLU |
| Part-Of-Speech Tagging | Part-Of-Speech Tagging |
| Partial Derivative | Partial Derivative |
| Partially Observable Markov Decision Processes | Partially Observable Markov Decision Processes |
| Partition Function | Partition Function |
| Perceptron | Perceptron |
| Performance Measure | Performance Measure |
| Perplexity | Perplexity |
| Pointer Network | Pointer Network |
| Policy | Policy |
| Policy Gradient | Policy Gradient |
| Policy Iteration | Policy Iteration |
| Polynomial Kernel Function | Polynomial Kernel Function |
| Pooling | Pooling |
| Pooling Layer | Pooling Layer |
| Positive Definite Matrix | Positive Definite Matrix |
| Post-Pruning | Post-Pruning |
| Potential Function | Potential Function |
| Power Method | Power Method |
| Pre-Training | Pre-Training |
| Precision | Precision |
| Prepruning | Prepruning |
| Primal Problem | Primal Problem |
| Primary Visual Cortex | Primary Visual Cortex |
| Principal Component Analysis | Principal Component Analysis |
| Prior | Prior |
| Probabilistic Context-Free Grammar | Probabilistic Context-Free Grammar |
| Probabilistic Graphical Model | Probabilistic Graphical Model |
| Probabilistic Model | Probabilistic Model |
| Probability Density Function | Probability Density Function |
| Probability Distribution | Probability Distribution |
| Probably Approximately Correct | Probably Approximately Correct |
| Proposal Distribution | Proposal Distribution |
| Prototype-Based Clustering | Prototype-Based Clustering |
| Proximal Gradient Descent | Proximal Gradient Descent |
| Pruning | Pruning |
| Quadratic Loss Function | Quadratic Loss Function |
| Quadratic Programming | Quadratic Programming |
| Quasi Newton Method | Quasi Newton Method |
| Radial Basis Function | Radial Basis Function |
| Random Forest | Random Forest |
| Random Sampling | Random Sampling |
| Random Search | Random Search |
| Random Variable | Random Variable |
| Random Walk | Random Walk |
| Recall | Recall |
| Receptive Field | Receptive Field |
| Reconstruction Error | Reconstruction Error |
| Rectified Linear Unit | Rectified Linear Unit |
| Recurrent Neural Network | Recurrent Neural Network |
| Recursive Neural Network | Recursive Neural Network |
| Regression | Regression |
| Regularization | Regularization |
| Regularizer | Regularizer |
| Reinforcement Learning | Reinforcement Learning |
| Relative Entropy | Relative Entropy |
| Reparameterization | Reparameterization |
| Representation | Representation |
| Representation Learning | Representation Learning |
| Representer Theorem | Representer Theorem |
| Reproducing Kernel Hilbert Space | Reproducing Kernel Hilbert Space |
| Rescaling | Rescaling |
| Reset Gate | Reset Gate |
| Residual Connection | Residual Connection |
| Residual Network | Residual Network |
| Restricted Boltzmann Machine | Restricted Boltzmann Machine |
| Reward | Reward |
| Ridge Regression | Ridge Regression |
| Right Singular Vector | Right Singular Vector |
| Risk | Risk |
| Robustness | Robustness |
| Root Node | Root Node |
| Rule Learning | Rule Learning |
| Saddle Point | Saddle Point |
| Sample | Sample |
| Sample Complexity | Sample Complexity |
| Sample Space | Sample Space |
| Scalar | Scalar |
| Selective Ensemble | Selective Ensemble |
| Self Information | Self Information |
| Self-Attention | Self-Attention |
| Self-Organizing Map | Self-Organizing Map |
| Self-Training | Self-Training |
| Semi-Definite Programming | Semi-Definite Programming |
| Semi-Naive Bayes Classifiers | Semi-Naive Bayes Classifiers |
| Semi-Restricted Boltzmann Machine | Semi-Restricted Boltzmann Machine |
| Semi-Supervised Clustering | Semi-Supervised Clustering |
| Semi-Supervised Learning | Semi-Supervised Learning |
| Semi-Supervised Support Vector Machine | Semi-Supervised Support Vector Machine |
| Sentiment Analysis | Sentiment Analysis |
| Separating Hyperplane | Separating Hyperplane |
| Sequential Covering | Sequential Covering |
| Sigmoid Belief Network | Sigmoid Belief Network |
| Sigmoid Function | Sigmoid Function |
| Signed Distance | Signed Distance |
| Similarity Measure | Similarity Measure |
| Simulated Annealing | Simulated Annealing |
| Simultaneous Localization And Mapping | Simultaneous Localization And Mapping |
| Singular Value | Singular Value |
| Singular Value Decomposition | Singular Value Decomposition |
| Skip-Gram Model | Skip-Gram Model |
| Smoothing | Smoothing |
| Soft Margin | Soft Margin |
| Soft Margin Maximization | Soft Margin Maximization |
| Softmax | Softmax |
| Softmax Function | Softmax Function |
| Softmax Regression | Softmax Regression |
| Softplus Function | Softplus Function |
| Span | Span |
| Sparse Coding | Sparse Coding |
| Sparse Representation | Sparse Representation |
| Sparsity | Sparsity |
| Specialization | Specialization |
| Splitting Variable | Splitting Variable |
| Squashing Function | Squashing Function |
| Standard Normal Distribution | Standard Normal Distribution |
| State | State |
| State Value Function | State Value Function |
| State-Action Value Function | State-Action Value Function |
| Stationary Distribution | Stationary Distribution |
| Stationary Point | Stationary Point |
| Statistical Learning | Statistical Learning |
| Steepest Descent | Steepest Descent |
| Stochastic Gradient Descent | Stochastic Gradient Descent |
| Stochastic Matrix | Stochastic Matrix |
| Stochastic Process | Stochastic Process |
| Stratified Sampling | Stratified Sampling |
| Stride | Stride |
| Structural Risk | Structural Risk |
| Structural Risk Minimization | Structural Risk Minimization |
| Subsample | Subsample |
| Subsampling | Subsampling |
| Subset Search | Subset Search |
| Subspace | Subspace |
| Supervised Learning | Supervised Learning |
| Support Vector | Support Vector |
| Support Vector Expansion | Support Vector Expansion |
| Support Vector Machine | Support Vector Machine |
| Surrogat Loss | Surrogat Loss |
| Surrogate Function | Surrogate Function |
| Surrogate Loss Function | Surrogate Loss Function |
| Symbolism | Symbolism |
| Tangent Propagation | Tangent Propagation |
| Teacher Forcing | Teacher Forcing |
| Temporal-Difference Learning | Temporal-Difference Learning |
| Tensor | Tensor |
| Test Error | Test Error |
| Test Sample | Test Sample |
| Test Set | Test Set |
| Threshold | Threshold |
| Threshold Logic Unit | Threshold Logic Unit |
| Threshold-Moving | Threshold-Moving |
| Tied Weight | Tied Weight |
| Tikhonov Regularization | Tikhonov Regularization |
| Time Delay Neural Network | Time Delay Neural Network |
| Time Homogenous Markov Chain | Time Homogenous Markov Chain |
| Time Step | Time Step |
| Token | Token |
| Tokenization | Tokenization |
| Tokenizer | Tokenizer |
| Topic Model | Topic Model |
| Topic Modeling | Topic Modeling |
| Trace | Trace |
| Training | Training |
| Training Error | Training Error |
| Training Sample | Training Sample |
| Training Set | Training Set |
| Transductive Learning | Transductive Learning |
| Transductive Transfer Learning | Transductive Transfer Learning |
| Transfer Learning | Transfer Learning |
| Transformer | Transformer |
| Transformer Model | Transformer Model |
| Transpose | Transpose |
| Transposed Convolution | Transposed Convolution |
| Trial And Error | Trial And Error |
| Trigram | Trigram |
| Turing Machine | Turing Machine |
| Underfitting | Underfitting |
| Undersampling | Undersampling |
| Undirected Graphical Model | Undirected Graphical Model |
| Uniform Distribution | Uniform Distribution |
| Unigram | Unigram |
| Unit | Unit |
| Universal Approximation Theorem | Universal Approximation Theorem |
| Universal Approximator | Universal Approximator |
| Universal Function Approximator | Universal Function Approximator |
| Unknown Token | Unknown Token |
| Unsupervised Layer-Wise Training | Unsupervised Layer-Wise Training |
| Unsupervised Learning | Unsupervised Learning |
| Update Gate | Update Gate |
| Upsampling | Upsampling |
| V-Structure | V-Structure |
| Validation Set | Validation Set |
| Validity Index | Validity Index |
| Value Function Approximation | Value Function Approximation |
| Value Iteration | Value Iteration |
| Vanishing Gradient Problem | Vanishing Gradient Problem |
| Vapnik-Chervonenkis Dimension | Vapnik-Chervonenkis Dimension |
| Variable Elimination | Variable Elimination |
| Variance | Variance |
| Variational Autoencoder | Variational Autoencoder |
| Variational Inference | Variational Inference |
| Vector | Vector |
| Vector Space Model | Vector Space Model |
| Version Space | Version Space |
| Viterbi Algorithm | Viterbi Algorithm |
| Vocabulary | Vocabulary |
| Warp | Warp |
| Weak Learner | Weak Learner |
| Weakly Supervised Learning | Weakly Supervised Learning |
| Weight | Weight |
| Weight Decay | Weight Decay |
| Weight Sharing | Weight Sharing |
| Weighted Voting | Weighted Voting |
| Whitening | Whitening |
| Winner-Take-All | Winner-Take-All |
| Within-Class Scatter Matrix | Within-Class Scatter Matrix |
| Word Embedding | Word Embedding |
| Word Sense Disambiguation | Word Sense Disambiguation |
| Word Vector | Word Vector |
| Zero Padding | Zero Padding |
| Zero-Shot Learning | Zero-Shot Learning |
| Zipf’s Law | Zipf’s Law |
Today’s benefit has been delivered 
Scan the QR code below to follow the Management Science Academy, and reply “benefit” in the background to receive nine major data for free 
No tricks, just directly receive
