DL Algorithms

- Overview

Deep learning uses artificial neural networks to perform complex calculations on large amounts of data. It is a machine learning based on the structure and function of the human brain. Deep learning algorithms train machines by learning from examples. Industries such as healthcare, e-commerce, entertainment, and advertising commonly use deep learning.

The structure of a neural network is similar to that of the human brain, consisting of artificial neurons, also known as nodes. These nodes are stacked next to each other in three layers: input layer, hidden layer, output layer.

Data provides information for each node in the form of input. This node multiplies the input with random weights, computes them, and adds a bias. Finally, a non-linear function, also known as an activation function, is used to determine which neurons to fire.

- Deep Learning Algorithms

While deep learning algorithms have self-learning representations, they rely on artificial neural networks that mirror the way the brain computes information. During training, the algorithm uses unknown elements in the input distribution to extract features, group objects, and discover useful data patterns. Like training a machine to teach itself, this happens at multiple levels, using algorithms to build models. 

Deep learning models utilize several algorithms. While no network is considered perfect, some algorithms are better suited to perform specific tasks. To choose the right algorithm, it is best to have a solid understanding of all major algorithms.

- Most Popular Deep Learning Algorithms

Here is a list of the 10 most popular deep learning algorithms:

• Convolutional Neural Network (CNN)
• Long Short Term Memory (LSTM)
• Recurrent Neural Network (RNN)
• Generative Adversarial Networks (GANs)
• Radial Basis Function Network (RBFN)
• Multilayer Perceptron (MLP)
• Self-Organizing Map (SOM)
• Deep Belief Network (DBN)
• Restricted Boltzmann Machine (RBM)
• Autoencoder

Deep learning algorithms can process almost any type of data and require massive amounts of computing power and information to solve complex problems. 

[More to come ...]