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 JAX

JAX 是由 Google 開發的開源庫，主要用於高性能數值計算和機器學習。它的名稱代表了「Just After eXecution」，意指其能夠在執行時進行優化，尤其是在自動微分方面。

JAX 結合了 NumPy 的易用性和 TensorFlow 的強大能力，允許用戶通過簡單的 Python 代碼進行高效計算。它的一個重要特性是支持自動微分，使得用戶能夠輕鬆計算梯度。

JAX 使用了一個名為 XLA（Accelerated Linear Algebra）的編譯器，可以將用戶的 Python 函數轉換為高效的機器代碼，從而加速計算過程。這種優化使得 JAX 在處理大規模數據時表現優異。

JAX 廣泛應用於機器學習、科學計算和數值優化等領域。許多前沿研究和應用都採用了 JAX，特別是在深度學習、強化學習和生成模型等方面。

未來，JAX 可能會繼續擴展其功能，吸引更多開發者和研究者使用。隨著機器學習和人工智能的持續發展，JAX 也可能在性能和易用性方面持續改進。

儘管 JAX 有許多優點，如高性能和靈活性，但也存在一些缺點。對於初學者來說，學習曲線可能較為陡峭，特別是對那些不熟悉 NumPy 或機器學習的用戶。此外，JAX 的生態系統相較於其他框架（如 TensorFlow 或 PyTorch）仍在不斷發展中，某些功能可能尚未完善。