Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain in order to “learn” from large amounts of data. While a neural network with a single layer can still make approximate predictions, additional hidden layers can help optimize accuracy.

Deep learning drives many artificial intelligence (AI) applications and services that improve automation, performing analytical and physical tasks without human intervention. Deep learning technology lies behind everyday products and services (such as digital assistants, voice-enabled TV remotes, and credit card fraud detection) as well as emerging technologies (such as self-driving cars).

It’s called “deep learning” because it uses deep neural networks, where “deep” refers to the number of layers through which the data is transformed. More layers allow for more complexity.

Deep learning algorithms use a technique called backpropagation, which adjusts the model parameters in accordance with the error in predictions. These algorithms feed data into the network and tweak the weights and biases of the model through gradient descent or variations of it.

Applications of deep learning are wide-ranging and include:

• Image and voice recognition
• Medical diagnosis and prognosis
• Financial forecasting
• Natural language processing
• Video game AI development
• And many more

Deep learning models require a large amount of data and substantial computing power to run efficiently, which is why GPUs and specialized hardware are often used.

The design and tuning of deep learning models can be a complex and specialized task, requiring knowledge of various network architectures, hyperparameters, loss functions, optimization strategies, and often an understanding of the specific domain to which the model is being applied.