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

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

B

BERT Boosting Backpropagation Batch Normalization Bias

C

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

D

Discriminative Model Deterministic Model Deep Learning Data Augmentation Deepfake

E

Epoch Encoder Ensemble Learning Explainable AI (XAI)Embedding

F

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

G

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

H

Hierarchical Model Hyperparameter Hallucination Heuristic Hidden Layer

I

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

J

JAX JSONL / JSON-lines Juxtaposition Jittering Joint Embedding

K

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

L

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

M

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

N

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

O

One-hot Encoding Overfitting Objective Function Optimizer Online Learning

P

Prompt Parameter Pretraining Policy / Reinforcement Learning Policy Pooling

Q

Q-learning Query Queue / Buffer Quantization Quality Estimation

R

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

S

Softmax Sampling Supervised Learning Sequence Modeling Self-Supervised Learning

T

Tokenizer Transformer Tuning / Hyperparameter Tuning Transfer Learning Training Data

U

U-Net Uncertainty Estimation Underfitting Universal Approximation Theorem Unsupervised Learning

V

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

W

Weight Decay Word Embedding Workflow Whitening / Whitening Transformation Weak Supervision

X

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

Y

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

Z

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

什么是 JAX

JAX 是一个由 Google 开发的开源库，主要用于高性能数值计算和机器学习。它的名称代表了“Just After eXecution”，意指其能够在运行时进行优化，尤其是在自动微分方面。

JAX 结合了 NumPy 的易用性和 TensorFlow 的强大能力，允许用户通过简单的 Python 代码进行高效计算。它的一个重要特性是支持自动微分，使用户能够轻松计算梯度。

JAX 使用了一个名为 XLA（Accelerated Linear Algebra）的编译器，可以将用户的 Python 函数转换为高效的机器代码，从而加速计算过程。这种优化使得 JAX 在处理大规模数据时表现优异。

JAX 广泛应用于机器学习、科学计算和数值优化等领域。许多前沿研究和应用都采用了 JAX，特别是在深度学习、强化学习和生成模型等方面。

未来，JAX 可能会继续扩展其功能，吸引更多开发者和研究者使用。随着机器学习和人工智能的不断发展，JAX 也可能在性能和易用性方面持续改进。

尽管 JAX 有许多优点，如高性能和灵活性，但也存在一些缺点。对于初学者来说，学习曲线可能较为陡峭，尤其是对那些不熟悉 NumPy 或机器学习的用户。此外，JAX 的生态系统相较于其他框架（如 TensorFlow 或 PyTorch）仍在不断发展中，某些功能可能尚未完善。