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Subject 5. Neural Networks, Deep Learning Nets, and Reinforcement Learning PDF Download
Neural NetworksNeural networks are a series of algorithms that mimic the operations of a human brain to recognize relationships between vast amounts of data.
A neural network contains layers of interconnected nodes. Each node is a perceptron and is similar to a multiple linear regression. The perceptron feeds the signal produced by a multiple linear regression into an activation function that may be nonlinear.
In a multi-layered perceptron (MLP), perceptrons are arranged in interconnected layers. The input layer collects input patterns. The output layer has classifications or output signals to which input patterns may map. For instance, the patterns may comprise a list of quantities for technical indicators about a security; potential outputs could be "buy," "hold" or "sell."
Hidden layers fine-tune the input weightings until the neural network's margin of error is minimal. It is hypothesized that hidden layers extrapolate salient features in the input data that have predictive power regarding the outputs. This describes feature extraction, which accomplishes a utility similar to statistical techniques such as principal component analysis.
Deep Learning NetsNeural networks with many hidden layers (at least 3 but often more than 20) are known as deep learning nets (DLNs). They are backbone of the artificial intelligence revolution.
In DLNs, each layer of nodes trains on a distinct set of features based on the previous layer's output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer.
This is known as feature hierarchy, and it is a hierarchy of increasing complexity and abstraction. It makes DLNs capable of handling very large, high-dimensional data sets with billions of parameters that pass through nonlinear functions.
Reinforcement LearningReinforcement Learning(RL) is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences.
While the goal in unsupervised learning is to find similarities and differences between data points, in reinforcement learning the goal is to find a suitable action model that would maximize the total cumulative reward of the agent.
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