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Glossary

0-9
0-shot learning1-shot learning2-stage detector3D convolution4D data5G + AI6DoF pose estimation7D representation8-bit quantization9-layer network
A
AGI / Artificial General IntelligenceAlgorithmArtificial Intelligence (AI)AttentionAutoencoder
B
BackpropagationBatch NormalizationBERTBiasBoosting
C
ChatbotClassifier / ClassificationClusteringCNN / Convolutional Neural NetworkCross-Validation
D
Data AugmentationDeep LearningDeepfakeDeterministic ModelDiscriminative Model
E
EmbeddingEncoderEnsemble LearningEpochExplainable AI (XAI)
F
Feature ExtractionFine-tuningForward PropagationFoundation ModelFusion / Multimodal Fusion
G
GAN / Generative Adversarial NetworkGenerative AIGradient DescentGraph Neural Network (GNN)Grounding
H
HallucinationHeuristicHidden LayerHierarchical ModelHyperparameter
I
Imbalanced DataInstance / SampleInstruction tuningIntelligence Amplification / AugmentationInterpretability
J
JAXJitteringJoint EmbeddingJSONL / JSON-linesJuxtaposition
K
K-means ClusteringK-Shot LearningKernel TrickKL Divergence (Kullback–Leibler Divergence)Knowledge Distillation
L
Large Language Model (LLM)Latent VariableLearning RateLoss FunctionLSTM / Long Short-Term Memory
M
Machine Learning (ML)Meta-learningModelMulti-head AttentionMultimodal / Multimodality
N
Neural NetworkNLP / Natural Language ProcessingNLU / Natural Language UnderstandingNormalizationNovelty Detection / Anomaly Detection
O
Objective FunctionOne-hot EncodingOnline LearningOptimizerOverfitting
P
ParameterPolicy / Reinforcement Learning PolicyPoolingPretrainingPrompt
Q
Q-learningQuality EstimationQuantizationQueryQueue / Buffer
R
RegularizationReinforcement Learning (RL)Representation LearningRetrieval Augmented Generation (RAG)RNN / Recurrent Neural Network
S
SamplingSelf-Supervised LearningSequence ModelingSoftmaxSupervised Learning
T
TokenizerTraining DataTransfer LearningTransformerTuning / Hyperparameter Tuning
U
U-NetUncertainty EstimationUnderfittingUniversal Approximation TheoremUnsupervised Learning
V
Validation SetVanishing / Exploding GradientVariational Autoencoder (VAE)Vector EmbeddingVision Transformer (ViT)
W
Weak SupervisionWeight DecayWhitening / Whitening TransformationWord EmbeddingWorkflow
X
X-axis / feature axisXAI / Explainable AIXLMXLNetXOR problem
Y
Y-axis / feature axisY-transform / YUVYAGNI (You Aren't Gonna Need It)Yield (model yield / throughput)Yoga of AI
Z
Z-score NormalizationZero-centric / Zero-bias initializationZero-gradient phenomenonZero-shot Learning / Zero-shot inferenceZygosity in augmentation

What is Validation Set

The Validation Set is a critical concept in machine learning and deep learning. It serves as a part of the dataset that is split into training, validation, and test sets, used to adjust the model's hyperparameters and evaluate its performance. By utilizing a validation set, researchers can monitor the model's performance during training, thereby avoiding overfitting and ensuring that the model works effectively on unseen data.


The use of a validation set is vital for improving the accuracy and generalization ability of a model. It provides a mechanism for conducting multiple trials and adjustments during the training process. Without a validation set, developers may struggle to effectively identify weaknesses in the model, leading to inefficient model design and erroneous decisions.


In a typical machine learning workflow, the dataset is first divided into training, validation, and test sets. The training set is used to train the model, the validation set is used for model tuning, and the test set is used for final performance evaluation. Generally, the size of the validation set is about 10%-20% of the dataset. During training, developers use the results from the validation set to determine whether to adjust the model's parameters.


Validation sets are widely used in various fields such as image recognition, natural language processing, and recommendation systems. For example, when using convolutional neural networks for image classification, developers can use the validation set to select the best learning rate and network architecture. Common machine learning libraries, such as TensorFlow and PyTorch, support the definition and use of validation sets.


As machine learning technology continues to evolve, the design and use of validation sets are also evolving. In the future, more automated validation methods may emerge, such as Bayesian optimization for hyperparameter search, further improving the efficiency and accuracy of models.


The primary advantage of a validation set is its ability to effectively monitor model performance and reduce the risk of overfitting. However, the downside is that if the validation set is poorly chosen, it may lead to inaccurate model tuning and even erroneous evaluations.


When creating a validation set, it is crucial to ensure its representativeness, so it can accurately reflect the model's performance in real-world applications. Moreover, it is important to avoid excessive tuning on the validation set to prevent bias from being introduced.

What is Validation Set - Glossary