Models

AI and Big Data Common Crawl Datasets Data Poisoning Ego 4D SQuAD The Pile Imbalanced Data Synthetic Data for AI Training

Ablation Active Learning (Machine Learning)Adversarial Machine Learning Affective AI AI Agents AI and Education AI and Finance AI and Medicine AI Assistants AI Ethics AI Generated Music AI Hallucinations AI Hardware AI in Customer Service AI Recommendation Algorithms AI Video Generation AI Voice Transfer Approximate Dynamic Programming Artificial Super Intelligence Backpropagation Bayesian Machine Learning Binary Classification AI Chatbots 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 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 Hidden Layer 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)Objective Function Precision and Recall Pretraining Recurrent Neural Networks Transformers Unsupervised Learning Voice Cloning Zero-shot Classification Models

Cognitive Architectures Keras Matplotlib 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 Attention Mechanisms Autoregressive Model Batch Gradient Descent Beam Search Algorithm Benchmarking Candidate Sampling Capsule Neural Network Causal Inference Classification Clustering Algorithms Cognitive Computing Cognitive Map Computational Creativity Computational Phenotyping Conditional Variational Autoencoders Concatenative Synthesis Context-Aware Computing Contrastive Learning CURE Algorithm Data Augmentation Deepfake Detection Diffusion Domain Adaptation Double Descent End-to-end Learning Evolutionary Algorithms Expectation Maximization Feature Store for Machine Learning Flajolet-Martin Algorithm Forward Propagation Gaussian Processes Generative Adversarial Networks (GANs)Gradient Boosting Machines (GBMs)Gradient Clipping Gradient Scaling Grapheme-to-Phoneme Conversion (G2P)Grounding Hyperparameters Homograph Disambiguation Hooke-Jeeves Algorithm Instruction Tuning Keyphrase Extraction Knowledge Distillation Knowledge Representation and Reasoning k-Shingles Latent Dirichlet Allocation (LDA)Markov Decision Process Metaheuristic Algorithms Mixture of Experts Model Interpretability Multimodal AI Neural Radiance Fields Neural Text-to-Speech (NTTS)One-Shot Learning Online Gradient Descent Out-of-Distribution Detection Overfitting and Underfitting Parametric Neural Networks Prompt Chaining Prompt Engineering Prompt Tuning Quantum Machine Learning Algorithms Regularization Representation Learning Retrieval-Augmented Generation (RAG)RLHF Semantic Search Algorithms Semi-structured data Sentiment Analysis Sequence Modeling Semantic Kernel Semantic Networks Statistical Relational Learning Symbolic AI Tokenization Transfer Learning Voice Cloning Winnow Algorithm Word Embeddings

Last updated on May 7, 20241 min read

Models

AI models are computer programs that analyze and find patterns in data to make predictions. Learn more about some of the notable models in the field.

In the heart of every AI system lies a model, acting as the brain, interpreting data, making predictions, and driving intelligent actions.

Foundation and Evolution:

Embark on a historical journey through AI modeling:

Early Designs: Explore models that laid the groundwork for AI, from perceptrons to basic neural networks.
Learning Paradigms: Understand the diverse learning strategies, be it supervised, unsupervised, or reinforcement learning.

Modern Marvels:

Navigate the contemporary world of AI modeling:

Deep Learning: Delve into the depths of convolutional neural networks, recurrent networks, and their multifaceted applications.
Transformers & Beyond: Uncover the architecture and power behind revolutionary models like GPT, BERT, and their successors.

Challenges and Frontiers:

Engage with the complexities and the future of AI modeling:

Overfitting & Generalization: Tackle the dichotomy of model specificity and adaptability.
Interpretable AI: Grasp the push for models that are not just smart but also transparent and explainable.

The "Models" section promises a voyage from the foundational pillars of AI modeling to the cutting-edge structures that are redefining what machines can achieve.

Unlock language AI at scale with an API call.

Get conversational intelligence with transcription and understanding on the world's best speech AI platform.

Sign Up Free Schedule a Demo