Neural Network (NN) - software constructions modeled after the way adaptable neurons in the brain were understood to work instead of human guided rigid instructions.

Deep Learning - a type of neural network, the subset of machine learning composed of algorithms that permit software to train itself to perform tasks by processing multilayered networks of data.

Machine Learning - computers' ability to learn without being explicity programed, with more than fifteen different approaches like Random Forest, Bayesian networks, Suport Vector machine uses, computer algorithms to learn from examples and experiences (datasets) rather than predefined, hard rules-based methods.

Supervised Learning - an optimization , trial - and - error process based on labeled data , algorithm comparing outputs with the correct outputs during training.

Unsupervised Learning - training samples are not labeled , the algorithm just looks for patterns, teaches itself.

Convolutional Neural Network - using the principle of convolution, a mathematical operation that basically takes two functions to produce a third one,​ instead of feeding in the entire dataset, it is broken into overlapping tiles​ with small neural networks and maxpooling used especially for images.

Natural Language Processing - a machine's attemp to "understand" speech or writen languages like humans.

Generative Adversarial Networks - a pair of jointly trained neural networks , one generative and the other discriminative, whreby the former generates fake images and the latter tries to distinguish them from real images.

Reinforcement Learning - a type of machine learning that shifts the focus to an abstract goal or decision making, a technology , a technology for learning and executiong actions in the real world.

Recurrent Neural Networks - for task than involve sequential inputs, like speech or language, this neural network processes an input sequence one element at a time.

Backpropagation - an algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation on the previous layer passing values backward through the network; how the synapses get updated over time; signals are automatically sent back through the network to update and adjust the weighting values.

Representation Learning - set of methods that allow a machine with raw data to automatically discover the representations needed for detection or classification.

Transfer Learning - the ability of an AI to learn from different tasks and apply its precedent knowledge to a completely new task.

General Artificial Intelligence - perform a wide range of tasks, including, including any human task, without being explicitly programmed.