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GenAI Dictionary

1. Generative AI (GenAI)2. Large Language Model (LLM)3. Natural Language Processing (NLP)4. Transformer Model 5. Hallucinatio 6. Prompt Engineering 7. Fine-Tuning 8. Zero-Shot Learning 9. Few-Shot Learning 10. Multimodal AI 11. Tokenization 12. Embedding 13. Bias in AI 14. Overfitting 15. Underfitting 16. Latent Space 17. Autoencoder 18. Diffusion Models 19. Generative Adversarial Network (GAN)20. Self-Supervised Learning 21. Artificial General Intelligence (AGI)22. Autoregressive Model 23. Chain-of-Thought Prompting 24. Context Window 25. Data Augmentation 26. Deep Learning 27. Energy-Based Model (EBM)28. Explainability/Interpretability 29. Generalization 30. Gradient Descent 31. Hyperparameter 32. Latent Variable Model 33. Likelihood Function 34.Markov Chain Monte Carlo (MCMC)35.Normalizing Flows 36. Perplexity 37. Regularization 38. Reinforcement Learning 39. Semi-Supervised Learning 40. Variational Autoencoder (VAE)41. Attention Mechanism 42. Backpropagation 43. Batch Normalization 44. Beam Search 45. Capsule Network 46. Dropout 47. Early Stopping 48. Epoch 49. Gradient Clipping 50. Hyperparameter Tuning 51. Instance Normalization 52. Learning Rate 53. Long Short-Term Memory (LSTM)54. Max Pooling 55. Mini-Batch Gradient Descent 56. Neural Architecture Search (NAS)57. Overparameterization 58. Residual Network (ResNet)59. Sparse Coding 60. Weight Initialization 61. Adversarial Training 62. Autoencoder 63. Bias in AI 64. Chain-of-Thought Prompting 65. Context Window 66. Deep Learning 67. Emergent Behavior 68. Explainability/Interpretability 69. Generalization 70. Gradient Descent 71. Hallucination 72. Hyperparameter 73. Latent Space 74. Neural Network 75. Overfitting 76. Parameters 77. Reinforcement Learning 78. Self-Supervised Learning 79. Token 80. Transformer Model 81. Activation Function 82. Batch Size 83. Class Imbalance 84. Data Leakage 85. Ensemble Learning 86. Feature Engineering 87. Gradient Vanishing/Exploding 88. Hyperparameter Optimization 89. Instance Segmentation 90. K-Fold Cross-Validation 91. Learning Rate Decay 92. Model Drift 93. One-Hot Encoding 94. Precision-Recall Tradeoff 95. Quantization 96. Regularization Parameter 97. Semi-Structured Data 98. Transfer Learning 99. Underfitting 100. Weight Sharing 101. Artificial Neural Network (ANN)102. Augmented Intelligence 103. Bayesian Network 104. Conditional Generation 105. Deep Belief Network (DBN)106. Encoder-Decoder Architecture 107. Fuzzy Logic 108. Generator 109. GPT (Generative Pre-trained Transformer)110. Latent Dirichlet Allocation (LDA)111. Markov Chain 112. Naive Bayes Classifier 113. Overparameterization 114. Prompt Injection 115. Robustness 116. Sparse Coding 117. Turing Test 118. Unsupervised Learning 119. Variational Autoencoder (VAE)120. Zero-Shot Learning 121. Artificial Neural Network (ANN)122. Augmented Intelligence 123. Bayesian Network 124. Conditional Generation 125. Deep Belief Network (DBN)126. Encoder-Decoder Architecture 127. Fuzzy Logic 128. Generator 129. GPT (Generative Pre-trained Transformer)130. Latent Dirichlet Allocation (LDA)131. Markov Chain 132. Naive Bayes Classifier 133. Overparameterization 134. Prompt Injection 135. Robustness 136. Sparse Coding 137. Turing Test 138. Unsupervised Learning 139. Variational Autoencoder (VAE)140. Zero-Shot Learning 141. Artificial General Intelligence (AGI)142. Bias (in AI)143. Chain-of-Thought Prompting 144. Context Window 145. Emergent Behavior 146. Explainability/Interpretability 147. Generalization 148. Hallucination 149. Large Language Model (LLM)150. Natural Language Processing (NLP)151. Neural Networks 152. Parameters 153. Reinforcement Learning 154. Token 155. Transformer Model 156. Zero-Shot Learning 157. Deep Learning 158. Fine-Tuning 159. Prompt Engineering 160. Retrieval-Augmented Generation (RAG)161. Active Learning 162. Attention Mechanism 163. Backpropagation 164. Capsule Network 165. Data Augmentation 166. Dropout 167. Embedding 168. Few-Shot Learning 169. Gradient Clipping 170. Hyperparameter Tuning 171. Instance-Based Learning 172. Jacobian Matrix 173. Knowledge Distillation 174. Long Short-Term Memory (LSTM)175. Meta-Learning 176. Neuroevolution 177. One-Shot Learning 178. Pruning 179. Quantitative Analysis:180. Recurrent Neural Network (RNN)181. Adversarial Training 182. Attention Head 183. Bidirectional Encoder Representations from Transformers (BERT)184. Contrastive Learning 185. Diffusion Models 186. Embedding Space 187. Federated Learning 188. Gradient Descent 189. Hyperparameter 190. Instance Normalization 191. Jacobian Regularization 192. Knowledge Graph 193. Layer Normalization 194. Mixture of Experts (MoE)195. Neural Architecture Search (NAS)196. Orthogonality Regularization 197. Positional Encoding 198. Quantile Regression 199. Residual Connection 200. Self-Attention 201. Active Learning 202. Attention Mechanism 203. Backpropagation 204. Capsule Network 205. Data Augmentation 206. Dropout 207. Embedding 208. Few-Shot Learning 209. Gradient Clipping 210. Hyperparameter Tuning 211. Instance-Based Learning 212. Jacobian Matrix 213. Knowledge Distillation 214. Long Short-Term Memory (LSTM)215. Meta-Learning 216. Neuroevolution 217. One-Shot Learning 218. Pruning 219. Quantitative Analysis 220. Recurrent Neural Network (RNN)221. Activation Function 222. Batch Normalization 223. Cross-Entropy Loss 224. Dimensionality Reduction 225. Epoch 226. Feature Engineering 227. Gradient Vanishing 228. Hyperplane 229. Instance Segmentation 230. Jittering 231. Kernel Trick 232. Learning Rate 233. Max Pooling 234. Nesterov Accelerated Gradient (NAG)235. Overfitting 236. Pooling Layer 237. Quantization 238. ReLU (Rectified Linear Unit)239. Stochastic Gradient Descent (SGD)240. Transfer Learning 241. Autoencoder 242. Bagging (Bootstrap Aggregating)243. Concept Drift 244. Decision Boundary 245. Ensemble Learning 246. Feature Map 247. Gradient Boosting 248. Information Gain 249. Jaccard Index 250. K-Means Clustering 251. Latent Variable 252. Markov Chain 253. Naive Bayes Classifier 254. Overfitting 255. Principal Component Analysis (PCA)256. Quantization 257. Reinforcement Learning 258. Support Vector Machine (SVM)259. Transfer Learning 260. Underfitting 261. Anthropomorphism 262. Bias (in AI)263. Chain-of-Thought Prompting 264. Context Window 265. Emergent Behavior 266. Hallucination (in AI)267. Parameters (in AI models)268. Token 269. Transformer Model 270. Artificial General Intelligence (AGI)271. Alignment (in AI)272. Compute 273. Data Science 274. Deepfake 275. Explainable AI (XAI)276. Human-in-the-Loop (HITL)277. Large Language Model (LLM)278. Neural Networks 279. Supervised Learning 280. Unsupervised Learning 281. Automatic Evaluation 282. Augmented Intelligence 283. Artificial Neural Network 284. CRM with AI 285. Generator 286. GPT (Generative Pre-trained Transformer)287. Machine Learning 288. Natural Language Processing (NLP)289. Parameters 290. Transformer 291. Deep Learning 292. Generative AI 293. Explainability/Interpretability 294. Generalization 295. Robustness 296. Data Privacy 297. AI Governance 298. Bias 299. Hallucination 300. Large Language Model (LLM)

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