Glossary

0-9

1-shot learning 2-stage detector 3D convolution 4D data 5G + AI 6DoF pose estimation 7D representation 8-bit quantization 9-layer network 0-shot learning

A

AGI / Artificial General Intelligence Artificial Intelligence (AI)Attention Algorithm Autoencoder

B

BERT Boosting Bias Backpropagation Batch Normalization

C

Cross-Validation Classifier / Classification CNN / Convolutional Neural Network Chatbot Clustering

D

Deepfake Discriminative Model Data Augmentation Deep Learning Deterministic Model

E

Epoch Explainable AI (XAI)Embedding Encoder Ensemble Learning

F

Forward Propagation Foundation Model Fine-tuning Feature Extraction Fusion / Multimodal Fusion

G

GAN / Generative Adversarial Network Grounding Graph Neural Network (GNN)Gradient Descent Generative AI

H

Hierarchical Model Hallucination Heuristic Hyperparameter Hidden Layer

I

Instruction tuning Imbalanced Data Instance / Sample Intelligence Amplification / Augmentation Interpretability

J

JAX JSONL / JSON-lines Juxtaposition Jittering Joint Embedding

K

K-Shot Learning Kernel Trick KL Divergence (Kullback–Leibler Divergence)K-means Clustering Knowledge Distillation

L

LSTM / Long Short-Term Memory Large Language Model (LLM)Learning Rate Loss Function Latent Variable

M

Meta-learning Multimodal / Multimodality Multi-head Attention Model Machine Learning (ML)

N

NLU / Natural Language Understanding Novelty Detection / Anomaly Detection Normalization Neural Network NLP / Natural Language Processing

O

One-hot Encoding Optimizer Objective Function Online Learning Overfitting

P

Prompt Pooling Parameter Policy / Reinforcement Learning Policy Pretraining

Q

Q-learning Quality Estimation Query Quantization Queue / Buffer

R

RNN / Recurrent Neural Network Reinforcement Learning (RL)Retrieval Augmented Generation (RAG)Regularization Representation Learning

S

Softmax Sampling Sequence Modeling Supervised Learning Self-Supervised Learning

T

Tokenizer Transformer Training Data Tuning / Hyperparameter Tuning Transfer Learning

U

U-Net Uncertainty Estimation Underfitting Unsupervised Learning Universal Approximation Theorem

V

Vision Transformer (ViT)Vector Embedding Vanishing / Exploding Gradient Variational Autoencoder (VAE)Validation Set

W

Weight Decay Workflow Weak Supervision Whitening / Whitening Transformation Word Embedding

X

X-axis / feature axis XLM XLNet XAI / Explainable AI XOR problem

Y

YAGNI (You Aren't Gonna Need It)Yoga of AI Y-transform / YUV Y-axis / feature axis Yield (model yield / throughput)

Z

Z-score Normalization Zygosity in augmentation Zero-centric / Zero-bias initialization Zero-gradient phenomenon Zero-shot Learning / Zero-shot inference

什麼是隱藏層

隱藏層是神經網絡中一個至關重要的組成部分，位於輸入層和輸出層之間。它負責捕捉輸入數據中的複雜模式和特徵，使網絡能夠有效學習和進行預測。

隱藏層中的每個神經元通過激活函數將輸入信號轉換為輸出信號。隱藏層的設計使模型能夠學習數據的高階特徵，從而提高其泛化能力和準確性。隱藏層的構建和調優是構建高效神經網絡的重要環節，通常涉及超參數的選擇和模型架構的實驗。

隨著深度學習的廣泛應用，隱藏層的研究和優化也在不斷發展。未來，隱藏層的設計可能會更加自動化，利用更先進的算法來優化網絡結構。同時，研究人員也在探索如何通過減少隱藏層的數量來提高模型的可解釋性。

隱藏層的優點在於能夠處理和學習複雜的數據模式，而缺點則在於增加了模型的複雜性，可能導致過擬合。在使用隱藏層時，需注意選擇適當的正則化方法和超參數調整，以提高模型的性能和穩定性。