This is an incomplete list of terms and definitions used in the field of machine learning. The majority of these definitions have been written by GPT-3.

Feel free to comment any terms which you think should be added.

**Artificial intelligence**

Artificial intelligence (AI) is the ability of a machine to imitate intelligent human behavior. This could involve learning, reasoning, and problem solving. AI technology is used in many fields, including medical diagnosis, stock trading, robot control, and law.

**Machine learning**

Machine learning is a subset of artificial intelligence that is concerned with the design and development of algorithms that allow computers to “learn” from data, without being explicitly programmed.

**Training data**

Training data is data that is used to train a machine learning model.

**Linear regression**

A mathematical model that attempts to describe the relationship between two variables by fitting a linear equation to observed data.

**Non-linear regression**

Non-linear regression is a statistical technique that can be used to model data that is not linearly related. Non-linear regression can be used to model data that is curvilinear, or data that has more than one independent variable.

**Deep learning**

Deep learning is a subset of machine learning that uses algorithms to model high-level abstractions in data. By using a deep learning model, a computer can learn to perform tasks such as image recognition and natural language processing.

**Overfitting**

Overfitting is when your model performs better on the training data than the test data. This usually happens when your model is too complex and is fitting to the noise in the training data rather than the actual signal.

**Loss function**

A loss function is a mathematical function that calculates the error between the predicted value and the actual value.

**GAN (Generative adversarial network)**

GANs are a type of neural network used for generative modeling. The main idea behind GANs is that you have two networks, a generator network that generates fake samples and a discriminator network that classifies samples as real or fake. The generator network is trained to fool the discriminator network. At the same time, the discriminator network is trained to correctly classify real and fake samples. As the training process unfolds, the generator network gets better and better at generating fake samples that look real to the discriminator network

**VAE (variational autoencoder)**

A variational autoencoder (VAE) is a type of artificial neural network used to learn efficient data encodings in an unsupervised manner. A VAE consists of an encoder and a decoder network, which work together to transform data from one representation to another. The encoder network compresses the data into a latent space, while the decoder network reconstructs the data from the latent space. The VAE is a generative model, which means that it can be used to generate new data samples from a given input. For example, if you give a VAE a picture of a face, it should be able to generate a new face that is similar to the input. VAEs have been found to be particularly effective at generating images.

**Latent space**

A latent space is a low–dimensional space in which the data points are “clustered“. In other words, it is a space in which similar data points are close together, and dissimilar data points are far apart. why do we use latent spaces in machine learning? There are a few reasons why we might want to use latent spaces in machine learning:

1. To reduce the dimensionality of the data. This can make the data easier to work with, and can also help to prevent overfitting.

2. To find clusters or groups in the data. This can be useful for visualisation, or for understanding the data better.

3. To make predictions. If we can find a low–dimensional latent space that captures the relationships between the data points, we may be able to use it to make predictions about new data points.