Deep Learning (DL)
Subfield of Machine Learning that uses artificial neural networks with many layers (deep) to learn extremely complex patterns directly from data.
Key Concepts
Artificial Neural Networks (ANNs)
The foundation of deep learning.
Hidden Layers
The layers between the input and output layers of an ANN.
Backpropagation
The algorithm that is used to train ANNs.
Activation Functions
Used to introduce non-linearity into the network.
Detailed Explanation
Deep learning is a subfield of machine learning that is concerned with the development of algorithms that are inspired by the structure and function of the brain. These algorithms are called artificial neural networks (ANNs).
ANNs are composed of a large number of interconnected processing elements (neurons) that work in unison to solve specific problems. ANNs are trained by a process of trial and error. During training, the weights of the connections between neurons are adjusted so that the network learns to produce the desired output for a given input.
Deep learning is a powerful technique that has been used to achieve state-of-the-art results on a wide variety of tasks, including image recognition, natural language processing, and machine translation.
Real-World Examples & Use Cases
Image Recognition
Deep learning is used to recognize objects in images. For example, it is used in self-driving cars to identify pedestrians and other vehicles.
Natural Language Processing
Deep learning is used to understand and generate human language. For example, it is used in machine translation to translate text from one language to another.
Machine Translation
Deep learning is used to translate text from one language to another. For example, Google Translate is a machine translation service that is powered by deep learning.