Understanding AI has become one of the most demanded skills across the industry. To fully comprehend the peculiarities of AI, deep learning models come in handy. Here are the top 5 deep learning models that will help make advanced AI.

Convolutional Neural Network Model

CNN is a type of neural network model which allows us to extract higher representations for the image content. CNN takes the image's raw pixel data, trains the model, and then extracts the features automatically for better classification.

Recurrent Neural Network Model

Recurrent neural networks (RNN) are a class of neural networks that are helpful in modeling sequence data. Derived from feedforward networks, RNNs exhibit similar behavior to how human brains function. It is one of the best deep learning models that will help make advanced AI.

Transformers Model

A transformer is a deep learning model that adopts the mechanism of self-attention, differentially weighting the significance of each part of the input data. It is used primarily in the fields of natural language processing and computer vision.

Autoencoders

An autoencoder is a neural network model that seeks to learn a compressed representation of the input. An autoencoder is a neural network that is trained to attempt to copy its input to its output. Autoencoders can be used for image denoising, image compression, and, in some cases, even generation of image data.

Generative Adversarial Network Model

A generative adversarial network (GAN) is one of the best deep learning models in which two neural networks compete with each other to become more accurate in their predictions. GANs typically run unsupervised and use a cooperative zero-sum game framework to learn.

Self Organizing Maps (SOMs)

Self Organizing Map is a type of Artificial Neural Network which is also inspired by biological models of neural systems from the 1970s. It follows an unsupervised learning approach and trained its network through a competitive learning algorithm.