Deep learning has wide application in artificial intelligence and computer vision backed programs. Across the world, machine learning has added more value to a range of tasks using key methodologies of artificial intelligence such as natural language processing, artificial neural networks and mathematical logics. Off lately, deep learning has become central to machine learning algorithms which are required to do highly complex computation and handle gigantic data.

With a multi-layer neural architecture, deep learning has been solving multiple scenarios and presenting solutions that work. There are several deep learning methods which are actively applied in machine learning and AI.

Types of Deep learning methods for AI programs

1. Convolutional Neural Networks (CNNs): CNNs, also known as ConvNets, are multilayer neural networks that are primarily used for image processing and object detection.

2. Long Short Term Memory Networks (LSTMs): Long-term dependencies may be learned and remembered using LSTMs, which are a kind of Recurrent Neural Network (RNN). Speech recognition, music creation, and pharmaceutical development are all common uses for LSTMs.

3. Recurrent Neural Networks (RNNs): Image captioning, time-series analysis, natural-language processing, handwriting identification, and machine translation are all typical uses for RNNs.

4. Generative Adversarial Networks (GANs): GANs are deep learning generative algorithms that generate new data instances that are similar to the training data. GANs aid in the creation of realistic pictures and cartoon characters, as well as the creation of photos of human faces and the rendering of 3D objects.

5. Radial Basis Function Networks (RBFNs): They are used for classification, regression, and time-series prediction and have an input layer, a hidden layer, and an output layer.

6. Multilayer Perceptrons (MLPs): MLPs are a type of feedforward neural network that consists of many layers of perceptrons with activation functions.

7. Self Organizing Maps (SOMs): SOMs enable data visualization by using self-organizing artificial neural networks to decrease the dimensionality of data. SOMs are designed to assist consumers in comprehending this multi-dimensional data.

8. Deep Belief Networks (DBNs): DBNs are generative models with several layers of stochastic, latent variables. For image identification, video recognition, and motion capture data, Deep Belief Networks (DBNs) are employed.

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