Fundamentals

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

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 Backpropagation Bayesian Machine Learning Binary Classification AI Conversational AI Convolutional Neural Networks 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 Hyperparameter Tuning Intelligent Document Processing Large Language Model (LLM)Loss Function Machine Learning Model Drift Multimodal Learning Natural Language Generation (NLG)Natural Language Processing (NLP)Natural Language Querying (NLQ)Natural Language Understanding (NLU)Precision and Recall Recurrent Neural Networks Transformers Unsupervised Learning Zero-shot Classification Models

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

Acoustic Models Activation Functions AdaGrad AI Alignment 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 Conditional Variational Autoencoders Concatenative Synthesis Contrastive Learning CURE Algorithm Data Augmentation Domain Adaptation Double Descent End-to-end Learning Evolutionary Algorithms Expectation Maximization 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 Keyphrase Extraction Knowledge Distillation k-Shingles Latent Dirichlet Allocation (LDA)Markov Decision Process Mixture of Experts Model Interpretability Multimodal AI Neural Radiance Fields Neural Text-to-Speech (NTTS)Online Gradient Descent Out-of-Distribution Detection Overfitting and Underfitting Parametric Neural Networks Prompt Chaining Prompt Engineering Prompt Tuning Regularization Representation Learning Retrieval-Augmented Generation (RAG)RLHF Semi-structured data Sentiment Analysis Sequence Modeling Semantic Kernel Semantic Networks Tokenization Transfer Learning Voice Cloning Winnow Algorithm Word Embeddings

Last updated on April 11, 20241 min read

Fundamentals

Unlock the mysteries of Artificial Intelligence with our 'Fundamentals' guide. Dive deep into the core concepts and discover the fascinating intersection of linguistics and AI.

The multifaceted world of Artificial Intelligence (AI) demands a firm grounding in its basic principles to fully appreciate its intricacies and potential.

Core Concepts:

Discover the essence of AI. This section offers a distilled overview of:

The definitions that capture the spirit of AI.
The evolution of AI through time.
The principles underpinning machine learning.
The pivotal role of neural networks in advancing AI capabilities.
The critical considerations of ethics in AI's design and deployment.

Language AI Focus:

Language and AI converge to create a dynamic realm of possibilities. Dive into:

The synergy of linguistics and AI.
The mechanisms that empower machines to process human language.
The challenges and future trajectories of AI's interaction with language.

Regardless of your background—be it a seasoned expert, academic, student, or just a curious soul—the "Fundamentals" section is your compass, directing you towards a deeper understanding of AI's vast domain.

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