Autoregressive models

Autoregressive models are a class of artificial intelligence (AI) and machine learning (ML) models that predict future values based on previous values in a sequence.

Parameter efficient tuning methods (PETM)

Parameter Efficient Tuning Methods (PETM) are techniques used in AI/ML to optimize model performance while minimizing computational resources.


Tuning refers to the process of optimizing the performance and parameters of artificial intelligence (AI) and machine learning (ML) models to achieve the desired outcomes.

Dialog-tuned language models

Dialog-tuned language models refer to a specialized class of AI models that have been fine-tuned to excel in generating human-like conversational responses.

Ebert test

The Ebert Test, proposed by film critic Roger Ebert at the 2011 TED conference, is a challenge directed at software developers to create a computer-based synthesized voice capable of telling jokes with such skill that it elicits genuine laughter from people.

A/B testing

A/B testing, also known as split testing, is a statistical method used to compare and evaluate the performance of two or more variants of a process, design, or user experience. A/B testing...


A chatbot is an AI-powered conversational agent designed to simulate human-like interactions through natural language processing (NLP) techniques.

Concept drift

Concept drift refers to the phenomenon in which the statistical properties of the target variable or input features in a machine learning model change over time.

Prompt engineering

Prompt engineering is a strategic approach used in the field of AI to optimize the performance and outputs of language models. It involves designing and refining the input prompts or queries given to these models to achieve desired results.

Abstract data type

An Abstract Data Type (ADT) is a high-level data structure that defines a set of operations and their behavior while abstracting away the implementation details.

Adaptive algorithms

Adaptive algorithms are a class of algorithms that have the ability to modify their behavior and parameters based on the input data they receive.

Stochastic gradient descent

Stochastic Gradient Descent (SGD) is a popular optimization algorithm in machine learning, specifically in training deep learning models. The goal of SGD, like other optimization algorithms, is to find the optimal parameters (e.g., weights and biases in a neural network) that minimize the loss function, a measure of the model's error on the training data.


In the context of machine learning and neural networks, parameters are the internal variables that the model learns during the training process. They are the part of the model that is optimized to improve the model's prediction performance.


A perceptron is a fundamental unit of the neural network which takes weighted inputs, processes it and is capable of performing binary classifications.

Convolutional neural networks

Convolutional Neural Networks (CNNs) are a specialized kind of neural network that is specifically suited to processing data that has a grid-like topology. CNNs are named after the mathematical operation "convolution," which is central to their functionality.

Long short-term memory

Long Short-Term Memory (LSTM) networks are a specialized type of recurrent neural network (RNN) designed to capture long-term dependencies in sequential data.


Backpropagation, short for "backward propagation of errors," is an algorithm commonly used to train artificial neural networks by iteratively adjusting the network's weights to minimize the difference between predicted and target outputs.

Recurrent neural networks

Recurrent neural networks (RNNs) are a type of artificial neural network designed to effectively process sequential data by utilizing recurrent connections.

Feedforward neural networks

Feedforward neural networks are a fundamental type of artificial neural network that process information in a unidirectional flow from the input to the output layer. They are capable of modeling complex relationships and have been widely used in various machine learning applications.

Reactive machines

Reactive machines are a type of artificial intelligence system that operates purely based on immediate inputs from the environment, without any memory or internal state. These machines respond to specific stimuli or inputs with pre-defined, programmed behaviors

Theory of mind

Theory of mind refers to the cognitive ability to attribute mental states, such as beliefs, desires, intentions, and emotions, to oneself and others, and to understand that others have different thoughts, knowledge, and perspectives from one's own.

Markov chain

A Markov Chain is a mathematical concept used to model a sequence of events or states, where the probability of transitioning from one state to another depends only on the current state and not on the past history.

Contrastive divergence

Contrastive Divergence (CD) is an algorithm used for training generative models, particularly in the context of Boltzmann Machines (BMs) and Restricted Boltzmann Machines (RBMs). CD is a practical approximation of the more...

Boltzmann machines

Boltzmann Machines (BMs) are stochastic neural networks inspired by the principles of statistical physics. They consist of a network of interconnected binary units known as neurons, organized into visible and hidden layers. Each neuron is associated with a weight that determines its influence on the overall behavior of the model.

Support vector machines

Support Vector Machines (SVMs) are powerful supervised learning models used for classification and regression tasks, especially in scenarios with high-dimensional data and non-linear relationships.


Transformers are advanced deep learning models. Unlike traditional models, transformers leverage self-attention mechanisms to process entire sequences in parallel, capturing intricate relationships between words, phrases, and sentences.

Natural language processing

Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that focuses on the interaction between computers and human language.

Data imputation

Data imputation refers to the process of filling in missing values in a dataset with estimated or predicted values.

Variational autoencoders

Variational autoencoders (VAEs) are generative models that combine the concepts of autoencoders and variational inference. They are neural network-based models that can learn a latent representation or code for input data, enabling them to generate new data samples similar to the training data.

Semi-supervised learning

Semi-supervised learning is a machine learning paradigm that combines labeled and unlabeled data to build predictive models.

One-shot learning

One-shot learning is a machine learning approach that aims to train models to recognize or classify new objects or concepts based on just a single or very limited number of examples.

Structured data

Structured data refers to data that is organized and formatted in a predefined manner, making it easily searchable, analyzable, and processable by machines.

Artificial intelligence

AI, or Artificial Intelligence, refers to the development and deployment of computer systems that can perform tasks that would typically require human intelligence.


Overfitting is a phenomenon that occurs in machine learning when a model performs exceptionally well on the training data but fails to generalize to new, unseen data.


In the context of AI, hallucinations refer to instances where artificial intelligence systems generate outputs or predictions that do not align with reality or contain misleading information.

Transformer model

A transformer model is a type of deep learning architecture based on a self-attention mechanism that allows it to capture relationships between words in a sequence of input data, such as sentences or documents. 

Reinforcement learning

Reinforcement learning is a subfield of machine learning that focuses on training agents to make decisions and take actions in an environment in order to maximize a cumulative reward.

Zero-shot learning

Zero-shot learning (ZSL) is a machine learning paradigm that allows a model to handle tasks for which it has seen no examples during training. The concept stems from human cognitive ability –...

Large language models

In the context of artificial intelligence and machine learning, an LLM typically refers to a Large Language Model. These models are trained on extensive amounts of text data and can generate human-like text. They are capable of tasks like translation, answering questions, writing essays, summarizing long documents, and even creating poetry or jokes

Data preprocessing

Data preprocessing is the critical step of transforming raw data into a clean and understandable format for machine learning (ML) models. Without this essential step, your ML model may stumble on irrelevant noise, misleading outliers, or gaping holes in your dataset, leading to inaccurate predictions and insights.

Synthetic data

Synthetic data refers to artificially generated information created via algorithms and mathematical models, rather than collected from real-world events. This data can represent a vast array of scenarios and conditions, offering a high degree of control over variables and conditions that would be difficult, if not impossible, to orchestrate in the real world.

Weak supervision

Weak supervision is a technique used in machine learning where the model is trained using a dataset that is not meticulously labeled. With weak supervision, less precise, noisy, or indirectly relevant labels are used instead.

Neural networks

Neural networks, inspired by the functioning of the human brain, are a form of machine learning architecture designed to 'think' and 'learn' from data. Comprising interconnected nodes, or 'neurons', these networks process input data, learning to recognize patterns and make decisions or predictions.

Deep learning

Deep learning is an AI technique that uses artificial neural networks with multiple layers (hence 'deep') to model and understand complex patterns in datasets. Each layer in the neural network corresponds to different features or more abstract representations, and they 'learn' from previous layers - similar to how our brain works.

Ensemble learning

Ensemble learning is an ML paradigm where multiple models, often referred to as 'base learners' or 'weak learners', are strategically generated and combined to solve a particular computational intelligence problem. The main principle behind ensemble learning is that a group of 'weak' models can come together to form a 'strong' model, improving prediction accuracy.

Computer vision

Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. By processing, analyzing, and understanding images or videos, computers can identify and classify objects, detect events, and even make decisions based on the visual data.

Hyperparameter tuning

Hyperparameter tuning is an important step in the process of building a machine learning model. It involves adjusting the configuration settings of the model prior to training in order to optimize its performance.

Predictive modeling

Predictive modeling is a statistical technique that uses mathematical algorithms and machine learning to forecast future outcomes based on historical and current data. It's akin to constructing a mathematical narrative of what has occurred in the past and applying it to the present to predict the future.

Federated learning

Federated learning is a machine learning approach that allows for the development of models across numerous decentralized devices or servers. These devices each hold local data samples and are networked together, enabling them to collaboratively learn from the data without actually exchanging it.

Data lake

At its core, a data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. Unlike traditional data management systems, which require the data to be structured and cleaned before storage, data lakes retain data in its raw form, offering businesses greater flexibility in terms of storage and access.