Deep Learning involves many layers of neural networks, also called “deep neural networks”, which are excellent for capturing complex patterns from very large amounts of data, including and especially unstructured data. The algorithms used in deep learning are largely inspired by the functioning of the human brain. Deep learning is used in any form of AI, including autonomous vehicles, face detection, translation, diagnosing diseases, fraud detection, natural language processing.
A neural network is made of layers, and layers are made of nodes (also called neurons or units). It is these nodes that process input data. First, each neuron that receives input. If this is the first layer, the input is raw data. If it is a higher order layer, the input comes from the layer below. The node applies weights to the inputs, adds a bias term and then applies a nonlinear activation function (such as ReLU or sigmoid) and produces its outputs the resulting value to the next layer, which receives it as input. The keyword here is “nonlinear” because in real life things are rarely linear. See nonlinearity in neural networks.
So in other words, input layer takes in raw data. Hidden layers extract patterns, features by making computations. Output layers produce output, which means come up with labels (classifications) or predictions. Methods like back propagation (learning from errors by adjusting weights based on errors), optimization (using algorithms to find best weight for a network) is used during learning.
Some popular deep learning methods are CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), sequential data such as time series or text LSTM (Long Term Short Memory), Long Term Sequence Modeling, Language models.
Post By: A. Tuter
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