Deep learning models are widely used by industries that solve complex problems

Deep learning has gained massive popularity in scientific computing, and deep learning models are widely used by industries that solve complex problems. Deep learning uses artificial neural networks to perform sophisticated computations on large amounts of data. It is a type of machine learning that works based on the structure and function of the human brain. Deep learning has offered noteworthy capabilities and advances in voice recognition, image comprehension, self-driving car, natural language procession, search engine optimization, and more. 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. This article features the top 10 deep learning models that will help make advanced AI.

Feedforward Neural Network Model

Deep feedforward networks, also often called feedforward neural networks, or multilayer perceptrons (MLPs), are the quintessential deep learning models. These models are called feedforward because information flows through the function being evaluated from x, through the intermediate computations used to define f, and finally to the output y. It is one of the best 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. Unlike classical image recognition where you define the image features yourself, 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. It relies entirely on self-attention to compute representations of its input and output WITHOUT using sequence-aligned RNNs or convolution.

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. It is used for visualization and exploratory data analysis of high-dimensional datasets.

Radial Basis Function Networks

Radial basis function networks are a commonly used type of artificial neural network for function approximation problems. Radial basis function networks are distinguished from other neural networks due to their universal approximation and faster learning speed. It is one of the best deep learning models that will help make advanced AI.

Long Short-Term Memory Networks

Long short-term memory is an artificial recurrent neural network architecture used in the field of deep learning. Unlike standard feedforward neural networks, LSTM has feedback connections. It can process not only single data points but also entire sequences of data.

Restricted Boltzmann Machines

RBMs were invented by Geoffrey Hinton and can be used for dimensionality reduction, classification, regression, collaborative filtering, feature learning, and topic modeling. RBMs are a special class of Boltzmann Machines, and they are restricted in terms of the connections between the visible and the hidden units.