Deep learning is a subfield of machine learning that is based on artificial neural networks (ANNs) and is used to model complex patterns and relationships in large datasets. It is particularly useful for tasks such as image recognition, speech recognition, natural language processing, and prediction.
An artificial neural network is a system of algorithms that is designed to recognize patterns. It consists of layers of interconnected nodes, called “neurons,” which are modeled after biological neurons in the human brain. Each neuron receives input from other neurons, processes that input, and sends output to other neurons.
Deep learning neural networks are characterized by their depth, which refers to the number of layers in the network. A deep learning network can have dozens or even hundreds of layers, whereas a traditional neural network typically has only one or two layers. The additional layers in a deep learning network allow it to learn increasingly complex features of the input data, such as edges, textures, and shapes in images, or phonemes, words, and grammar in speech.
The process of training a deep learning neural network involves feeding it large amounts of labeled data, such as images with corresponding labels, and adjusting the parameters of the network (weights and biases) so that it can correctly classify new inputs. This process is done using an optimization algorithm, such as gradient descent, which aims to minimize the error between the network’s predictions and the correct labels.
Once the network is trained, it can be used to make predictions on new, unseen data. This can be done by forwarding the input through the network, and the output of the final layer is used as the prediction.
Deep learning has led to significant improvements in many areas such as image and speech recognition, and natural language processing and it is also used in many industries such as finance, healthcare, transportation, and many more.