What is deep learning?

Overview

Deep learning is a subset of artificial intelligence (AI) and machine learning. It uses both structured and unstructured data for training.

Deep learning uses a layer of the algorithm to create a neural network, which is an artificial replication and structure of the human brain.

The learning is called “deep” because, at each passing level, the neural network rapidly discovers new levels of data.

• A neural network learns through the process of back-propagation.

• Each time the data is trained with a large dataset of inputs, the efficiency is enhanced.

• Deep learning is based on the functionality of the human brain.

• It imitates the human brain’s functionality for managing data and forming patterns for decision making.

• The trained dataset can be interconnected, diverse, and complex.

• When creating deep learning algorithms, developers and engineers configure the number of layers and types of functions that connect each layer’s output to the input of the next layer.

Artificial neural network

Y=f(X1W1+X2W2+X3W3+…………+ XnWn+b)

Applications

Deep learning has proven to be very efficient in tasks like:

• Image captioning
• Voice recognition
• Facial recognition
• Medical imaging
• Language translation
• Self-driving cars

Note: Refer to the following article on neural networks for more information.

• What is the convolutional neural network?