LLM Collection

AI and Big Data Common Crawl Datasets Corpus in NLP Data Drift Data Poisoning Data Scarcity Ego 4D ImageNet SQuAD The Pile Imbalanced Data Machine Learning Bias Synthetic Data for AI Training Test Data Set Validation Data Set

Ablation Accuracy in Machine Learning Active Learning (Machine Learning)Adversarial Machine Learning Affective AI AI Agents AI and Education AI and Finance AI and Medicine AI Assistants AI Detection AI Ethics AI Generated Music AI Hallucinations AI Hardware AI in Customer Service AI Interpretability AI Lifecycle Management AI Literacy AI Monitoring AI Oversight AI Privacy AI Prototyping AI Recommendation Algorithms AI Regulation AI Resilience AI Robustness AI Safety AI Scalability AI Simulation AI Standards AI Steering AI Transparency AI Video Generation AI Voice Transfer Approximate Dynamic Programming Artificial Super Intelligence Backpropagation Bayesian Machine Learning Bias-Variance Tradeoff Binary Classification AI Chatbots Clustering in Machine Learning Composite AI Confirmation Bias in Machine Learning Conversational AI Convolutional Neural Networks Counterfactual Explanations in AI Curse of Dimensionality Data Labeling Deep Learning Deep Reinforcement Learning Differential Privacy Dimensionality Reduction Embedding Layer Emergent Behavior Entropy in Machine Learning Ethical AI Explainable AI F1 Score in Machine Learning F2 Score Feedforward Neural Network Fine Tuning in Deep Learning Gated Recurrent Unit Generative AI Graph Neural Networks Ground Truth in Machine Learning Hidden Layer Human Augmentation with AI Hyperparameter Tuning Intelligent Document Processing Large Language Model (LLM)Loss Function Machine Learning Machine Learning in Algorithmic Trading Model Drift Multimodal Learning Natural Language Generation (NLG)Natural Language Processing (NLP)Natural Language Querying (NLQ)Natural Language Understanding (NLU)Neural Text-to-Speech (NTTS)Neuroevolution Objective Function Precision and Recall Pretraining Recurrent Neural Networks Transformers Unsupervised Learning Voice Cloning Zero-shot Classification Models Machine Learning Neuron Reproducibility in Machine Learning Semi-Supervised Learning Supervised Learning Uncertainty in Machine Learning

Cognitive Architectures Keras Matplotlib MLOps Natural Language Toolkit (NLTK)NumPy Pandas PyTorch SciPy Scikit-learn Seaborn Python Package TensorFlow

Acoustic Models Activation Functions AdaGrad AI Alignment AI Emotion Recognition AI Guardrails AI Speech Enhancement Articulatory Synthesis Association Rule Learning Attention Mechanisms Augmented Intelligence Auto Classification Autoencoder Autoregressive Model Batch Gradient Descent Beam Search Algorithm Benchmarking Boosting in Machine Learning Candidate Sampling Capsule Neural Network Causal Inference Classification Clustering Algorithms Cognitive Computing Cognitive Map Collaborative Filtering Computational Creativity Computational Linguistics Computational Phenotyping Computational Semantics Conditional Variational Autoencoders Concatenative Synthesis Confidence Intervals in Machine Learning Context-Aware Computing Contrastive Learning Cross Validation in Machine Learning CURE Algorithm Data Augmentation Data Drift Decision Intelligence Decision Tree Deepfake Detection Diffusion Domain Adaptation Double Descent End-to-end Learning Ensemble Learning Epoch in Machine Learning Evolutionary Algorithms Expectation Maximization Feature Learning Feature Selection Feature Store for Machine Learning Federated Learning Few Shot Learning Flajolet-Martin Algorithm Forward Propagation Gaussian Processes Generative Adversarial Networks (GANs)Genetic Algorithms in AI Gradient Boosting Machines (GBMs)Gradient Clipping Gradient Scaling Grapheme-to-Phoneme Conversion (G2P)Grounding Human-in-the-Loop AI Hyperparameters Homograph Disambiguation Hooke-Jeeves Algorithm Hybrid AI Image Recognition Incremental Learning Inductive Bias Information Retrieval Instruction Tuning Keyphrase Extraction Knowledge Distillation Knowledge Representation and Reasoning k-Shingles Latent Dirichlet Allocation (LDA)Learning To Rank Learning Rate Logits Machine Learning Life Cycle Management Machine Learning Preprocessing Machine Translation Markov Decision Process Metaheuristic Algorithms Mixture of Experts Model Interpretability Monte Carlo Learning Multimodal AI Multi-task Learning Multitask Prompt Tuning Naive Bayes Classifier Named Entity Recognition Neural Radiance Fields Neural Style Transfer Neural Text-to-Speech (NTTS)One-Shot Learning Online Gradient Descent Out-of-Distribution Detection Overfitting and Underfitting Parametric Neural Networks Part-of-Speech Tagging Pooling (Machine Learning)Principal Component Analysis Prompt Chaining Prompt Engineering Prompt Tuning Quantum Machine Learning Algorithms Random Forest Rectified Linear Unit (ReLU)Regularization Representation Learning Restricted Boltzmann Machines Retrieval-Augmented Generation (RAG)RLHF Semantic Search Algorithms Semi-structured data Sentiment Analysis Sequence Modeling Semantic Kernel Semantic Networks Spike Neural Networks Statistical Relational Learning Symbolic AI Topic Modeling Tokenization Transfer Learning Vanishing and Exploding Gradients Voice Cloning Winnow Algorithm Word Embeddings

Last updated on June 24, 20241 min read

LLM Collection

Ever wanted to learn how to build an LLM Chatbot from scratch? Check out this article to learn how!

The table below includes some of the most influential models, and the order is sorted by their release dates.

Model	Release Date	Developer	License	Description
GPT-4	2023	OpenAI	Custom	Successor to GPT-3, built on a similar architecture but with improvements.
GPT-3	June 2020	OpenAI	Custom	175 billion parameters, known for its versatility and capability.
Turing-NLG	February 2020	Microsoft	Custom	17 billion parameters, aimed at natural language understanding and generation.
GPT-2	February 2019	OpenAI	Modified MIT	Initially withheld from public release due to concerns over potential misuse.
BERT	October 2018	Google	Apache 2.0	Designed to understand the context of words in search queries.
Transformer XL	January 2019	Google/CMU	Apache 2.0	Extended Transformer model to handle longer sequences of text.
GPT	June 2018	OpenAI	Modified MIT	First Generative Pre-trained Transformer with 117M parameters.
ELMo	March 2018	Allen Institute	Apache 2.0	Deep contextualized word representations, allowing for rich word meanings.

Mixture of Experts (MoE) is a method that presents an efficient approach to dramatically increasing a model’s capabilities without introducing a proportional amount of computational overhead. To learn more, check out this guide!

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