Understanding AI has quickly become one of the most in-demand skills in the industry. Deep learning models are useful for fully comprehending the peculiarities of AI. Here are the top five deep learning models that will aid in the development of advanced AI.
Convolutional Neural Network Model
CNN is a type of neural network model that allows us to extract more detailed representations of image content. CNN uses raw pixel data from an image to train the model, and then automatically extracts features for better classification.
Recurrent Neural Network Model
Recurrent neural networks (RNN) are a type of neural network that can help with sequence data modeling. RNNs, which are evolved from feedforward networks, act alike to human brains. It is one of the best deep learning models that will aid in the development of advanced AI.
Transformers Model
A transformer is a deep learning model that uses the self-attention mechanism to weigh the significance of each part of the input data differently. It is primarily used in natural language processing and computer vision.
Autoencoders
An autoencoder is a type of neural network model that attempts to learn a compressed representation of the input. An autoencoder is a neural network that has been trained to try to replicate its input to its output. Autoencoders can be used for image denoising, compression, and, in some cases, image data generation.
Generative Adversarial Network Model
A generative adversarial network (GAN) is one of the best deep learning models in which two neural networks compete to become more accurate in their predictions. GANs are typically run unsupervised and learn using a cooperative zero-sum game framework.
Self-Organizing Maps (SOMs)
Self-Organizing Map is a type of Artificial Neural Network that was inspired by biological models of neural systems developed in the 1970s. It employs unsupervised learning and trains its network using a competitive learning algorithm.
