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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 Tuning / Hyperparameter Tuning

Hyperparameter tuning is a crucial process in machine learning and deep learning that involves selecting the best hyperparameters for a model to improve performance. Hyperparameters are predefined settings that affect how a model learns and performs, distinct from model parameters such as weights. The selection of hyperparameters is vital in the machine learning workflow.


The choice of hyperparameters has a significant impact on the model’s performance. Proper hyperparameter tuning can greatly enhance prediction accuracy while minimizing the risks of overfitting or underfitting. Effective tuning leads to better performance on validation datasets, improving real-world application outcomes.


Common methods for hyperparameter tuning include Grid Search, Random Search, and Bayesian Optimization. Grid Search exhaustively evaluates all possible parameter combinations, while Random Search randomly selects combinations for assessment. Bayesian Optimization employs a probabilistic model to guide hyperparameter selection, often leading to quicker optimal solutions.


Hyperparameter tuning is indispensable in fields like image classification, natural language processing, and recommendation systems. For instance, when training Convolutional Neural Networks (CNNs), hyperparameters such as learning rate, batch size, and network depth need careful tuning for optimal performance.


As automated machine learning (AutoML) and deep learning evolve, hyperparameter tuning will become more intelligent and automated. Utilizing advanced techniques like evolutionary algorithms and reinforcement learning, future tuning processes will find ideal parameter combinations faster.


While hyperparameter tuning enhances model performance and accuracy, it can be time-consuming and computationally intensive. Choosing the right tuning methods and tools can help mitigate these issues.


When performing hyperparameter tuning, it’s crucial to maintain appropriate data splits (training, validation, and testing) to avoid data leakage and overfitting. Optimal hyperparameter values can vary across different datasets and tasks, necessitating careful selection.