Deep Learning

Deep learning is a method in artificial intelligence (AI) that teaches computers to process data in a way that is inspired by the human brain. Deep learning models can recognize complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions

• deep learning algorithms can be regarded both as a sophisticated and mathematically complex evolution of machine learning algorithms

• Describes algorithms that analyze data with a logic structure similar to how a human would draw conclusions.

• Can be supervised, unsupervised, semi-supervised, self-supervised, or reinforcement.

• To achieve this deep learning applications use a layered structure of algorithms called an artificial neural network (ANN).

Artificial Neural Network (ANN)

• Hidden layers are called hidden, because their values aren’t directly observed in the training data (they are calculated during the training process).

• In simple terms, the hidden layers are calculated values used by the network to make predictions.

• The more hidden layers a network has, the more complex the patterns it can recognize.

• ANNs are hungry for data. The more data you feed them, the better they perform.

Advantages of deep learning:

• Automatic feature extraction (no need to manually define features)

• High accuracy

CNN (Convolutional Neural Network)

CNNs are a type of deep learning neural network that have been successfully applied to analyzing visual imagery.