Deep Learning is a subfield of machine learning that focuses on algorithms based on artificial neural networks, particularly those networks with multiple layers, often referred to as deep neural networks. The term ‘deep’ refers to the number of layers in the network; the more layers, the deeper the network. Deep learning algorithms are adept at automatically learning representations from data, and have been instrumental in achieving state-of-the-art results in various domains, especially in tasks that involve high-dimensional data such as images, speech, and text.

Deep neural networks consist of layers of interconnected nodes or neurons, where each layer transforms its input data into a slightly more abstract representation. These layers stack up, allowing the network to learn increasingly complex features. For example, in image recognition, initial layers might learn to recognize edges, intermediate layers might recognize textures and shapes, and deeper layers might recognize complex objects.

Key components and architectures within Deep Learning include:

Convolutional Neural Networks (CNNs): Especially powerful for tasks like image recognition, CNNs include convolutional layers that automatically and adaptively learn spatial hierarchies from data.

Recurrent Neural Networks (RNNs): Suitable for sequence data such as time series or natural language, RNNs have loops to allow information persistence.

Long Short-Term Memory (LSTM): A special kind of RNN capable of learning long-term dependencies, which is useful in sequence prediction problems.

Generative Adversarial Networks (GANs): Consist of two networks, one generating candidates and the other evaluating them, and are used in unsupervised learning to generate new data that is similar to the training data.

Autoencoders: Neural networks that are used to learn efficient data codings in an unsupervised manner.

Deep Learning has been transformative in numerous applications such as image and speech recognition, natural language processing, and game playing (e.g., AlphaGo). However, training deep neural networks requires large datasets and significant computational power. Additionally, deep learning models are often criticized for being ‘black boxes’ as their internal workings can be hard to interpret, and they require careful tuning.