i NTD in AI: Early Stopping · Dark Matter Industries

NTD in AI: Early Stopping

Non-technical definitions in AI

Early Stopping is a technique used to [Regularize] a [Neural Network]. After each [Epoch] of training, the model’s performance is measured with the validation set and the model is saved (known as a checkpoint). As more Epochs of training is completed, the model will fit the data better and better (see Gradient Descent) until at some Epoch, the model will start to overfit the training data. At this point the validation error rate will start to rise. We use the model checkpoint before this observed overfitting.

See also Regularization, Dropout and Batch Normalisation.

Machine learning is a technical subject and the use of technical terms by engineers have the potential of coming between clear communication with non-engineers, especially in the business setting. In spare moments I started to put together simple, non-technical definitions of nouns and verbs used in the field of machine learning as a kind of Rosetta Stone for non-engineers.This is a work-in-progress which I may collect into a book one day. This is one of those definitions.

Other non-technical definitions:

  1. NTD in AI: 1 of K Encoding
  2. NTD in AI: Activation Function
  3. NTD in AI: Active Learning
  4. NTD in AI: Accuracy
  5. NTD in AI: Autoencoder
  6. NTD in AI: Backward Stepwise Selection
  7. NTD in AI: Bagging
  8. NTD in AI: Batch Normalization
  9. NTD in AI: Bayesian Hyperparameter Optimization
  10. NTD in AI: BERT
  11. NTD in AI: Best Subset Selection
  12. NTD in AI: Bias
  13. NTD in AI: Clustering
  14. NTD in AI: Collaborative Filtering
  15. NTD in AI: Confusion Set Disambiguation
  16. NTD in AI: Convolution Neural Network
  17. NTD in AI: Cosine Similarity
  18. NTD in AI: Cost-Sensitive Accuracy
  19. NTD in AI: Cloze Test
  20. NTD in AI: Credit Assignment Problem
  21. NTD in AI: Data Augmentation
  22. NTD in AI: Data Imputation
  23. NTD in AI: Dataset
  24. NTD in AI: DBSCAN
  25. NTD in AI: Decision Boundary
  26. NTD in AI: Decoder
  27. NTD in AI: Deep Learning
  28. NTD in AI: Denoising Autoencoder
  29. NTD in AI: Density Estimation
  30. NTD in AI: Domain Expert
  31. NTD in AI: Dropout
  32. NTD in AI: Early Stopping
  33. NTD in AI: Embedding
  34. NTD in AI: Encoder
  35. NTD in AI: Ensemble Learning
  36. NTD in AI: Expected Test MSE
  37. NTD in AI: Exploding Gradient
  38. NTD in AI: Feature
  39. NTD in AI: Feature Selection
  40. NTD in AI: Feed Forward Neural Network
  41. NTD in AI: Filter (Matrix)
  42. NTD in AI: Forward Propagation
  43. NTD in AI: Forward Stepwise Selection
  44. NTD in AI: Fully Connected Neural Network Layers
  45. NTD in AI: Fully Visible Belief Network
  46. NTD in AI: Fuzzy Set
  47. NTD in AI: Gated Recurrent Neural Network
  48. NTD in AI: Gaussian Kernel Regression
  49. NTD in AI: Gaussian Mixture Model
  50. NTD in AI: Generalize
  51. NTD in AI: Gradient
  52. NTD in AI: Gradient Boosting
  53. NTD in AI: Gradient Descent
  54. NTD in AI: Grid Search
  55. NTD in AI: Ground Truth
  56. NTD in AI: Hidden Layers
  57. NTD in AI: Hyperbolic Tangent (tanH)
  58. NTD in AI: Hyperparameter
  59. NTD in AI: Input Vectors
  60. NTD in AI: Intrinsic Motivation
  61. NTD in AI: Irreducible Errors
  62. NTD in AI: k-Means
  63. NTD in AI: Kernel (Trick)
  64. NTD in AI: Kernel Regression
  65. NTD in AI: Label/Labeled Examples
  66. NTD in AI: LambdaMART
  67. NTD in AI: Linear Models
  68. NTD in AI: Logistic Regression (Softmax)
  69. NTD in AI: Long Short Term Memory (LSTM)
  70. NTD in AI: Meta-Model
  71. NTD in AI: Manhattan Taxicab Norm
  72. NTD in AI: MNIST
  73. NTD in AI: Model Cards
  74. NTD in AI: Moment Matching
  75. NTD in AI: MP Neuron
  76. NTD in AI: Multi-Label Classification
  77. NTD in AI: Multi-Layer Perceptron
  78. NTD in AI: Munging
  79. NTD in AI: NADE
  80. NTD in AI: Non-Parametric Methods
  81. NTD in AI: Norm
  82. NTD in AI: Observation
  83. NTD in AI: One Class Classification
  84. NTD in AI: One-Hot Encoding
  85. NTD in AI: One Shot Learning
  86. NTD in AI: One Versus Rest
  87. NTD in AI: Oracle
  88. NTD in AI: Overfitting
  89. NTD in AI: Oversampling
  90. NTD in AI: Padding
  91. NTD in AI: Perceptron
  92. NTD in AI: Pooling
  93. NTD in AI: Prediction Strength
  94. NTD in AI: Predictors
  95. NTD in AI: Preprocessing
  96. NTD in AI: Principal Component Analysis (PCA)
  97. NTD in AI: Random Search
  98. NTD in AI: ReLU
  99. NTD in AI: Recurrent Neural Network (RNN)
  100. NTD in AI: ROC Curve
  101. NTD in AI: Semi-Supervised Learning
  102. NTD in AI: Sequence Labeling
  103. NTD in AI: Siamese Neural Network
  104. NTD in AI: SMOTE - Synthetic Minority Oversampling Technique
  105. NTD in AI: Softmax
  106. NTD in AI: Softplus
  107. NTD in AI: Stepwise Selection
  108. NTD in AI: Stride
  109. NTD in AI: Subset Selection
  110. NTD in AI: Supervised Learning
  111. NTD in AI: t-SNE
  112. NTD in AI: Target Vectors
  113. NTD in AI: Training Instance
  114. NTD in AI: Training Set
  115. NTD in AI: Triplet Loss Function
  116. NTD in AI: UMAP - Unifold Manifold Approximation and Projection
  117. NTD in AI: Unary Classification
  118. NTD in AI: Validation Set
  119. NTD in AI: Vanishing Gradient
  120. NTD in AI: Variational Autoencoder
  121. NTD in AI: Volume (Convolution)
  122. NTD in AI: Voting
  123. NTD in AI: WaveNet
  124. NTD in AI: Weak Learners
  125. NTD in AI: Word Embeddings
  126. NTD in AI: word2vec