For deeper networks, the obsession with image classification tasks seems to have also caused tutorials to appear on the more complex convolutional neural networks. This is great if you’re into that sort of thing, however, if someone is more interested in data with timeframes then recurrent neural networks (RNNs) come in handy.

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Deep reinforcement learning involves building a deep learning model which enables function approximation between the input features and future discounted rewards values also called Q values. We have seen how we can effectively get these q values and create a map consisting of input features and corresponding set of q values in this article.

This map of input features and all possible q values at a given state enables the Reinforcement learning agent get an overall picture of environment which further helps the agent in choosing the optimal path.

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A Recurrent Neural Network is a type of Artificial Neural Network that contains shared neuron layers between its inputs through time. This allows us to model temporal data such as video sequences, weather patterns or stock prices. There are many ways to design a recurrent cell, which controls the flow of information from one time-step to another. A recurrent cell can be designed to provide a functioning memory for the neural network. Two of the most popular recurrent cell designs are the Long Short-Term Memory cell (LSTM) and the Gated Recurrent Unit cell (GRU).

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A time series contains a sequence of data points observed at specific intervals over time. A time series prediction uses a model to predict future values based on previously observed values. The natural temporal order of time series data makes analysis of time series different from cross-sectional or spatial data analyses, neither of which depends on a time component.

Time series predictions can be useful in a variety of settings, from processing signal data streaming from a sensor at an industrial site to monitoring trends in a financial market or maintaining inventory in a commercial setting. In all these scenarios, recent data can be used to inform predictions about future goal values.

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Deep Recurrent Reinforcement Learning makes use of a Recurrent Neural Network (RNN), such as Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU) based networks, for learning a value function that maps environment states to action values. Recurrent Neural Networks are useful for modeling time-series data since the network maintains a memory, learning to retain useful information from inputs of prior model inferences. Every time the model is called, the memory is updated in correspondence with the current inputs.

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Deep Reinforcement Learning involves using a neural network as a universal function approximator to learn a value function that maps state-action pairs to their expected future reward given a particular reward function. This can be done many different ways. For example, a Monte Carlo based algorithm will observe total rewards following state-action pairs from a complete episode to make build training data for the neural network. Alternatively, a Temporal Difference approach would use incremental rewards from single time-steps and bootstrap off of predicted future rewards from the latest version of the value function model. However, no matter what approach is taken, it is important that the neural network is being efficiently fitted to the data in order to optimize the learning algorithm. There are many factors that determine a neural networks ability to fit to training data. In this post we will examine how scaling our outputs can affect our rate of convergence.

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In the light of problems caused due to poor crowd management, such as crowd crushes and blockages, there is an increasing need for computational models which can analyze highly dense crowds using video feeds from surveillance cameras. Crowd counting is a crucial component of such an automated crowd analysis system. This involves estimating the number of people in the crowd, as well as the distribution of the crowd density over the entire area of the gathering. Identifying regions with crowd density above the safety limit can help in issuing prior warnings and can prevent potential crowd crushes. Estimating the crowd count also helps in quantifying the significance of the event and better handling of logistics and infrastructure for the gathering.

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Deep Recurrent Neural Networks (RNN) are a type of Artificial Neural Network that takes the networks previous hidden state as part of its input, effectively allowing the network to have a memory. This makes RNNs useful for modeling sequential or time-series data such as stock prices. However, training RNNs on sequences greater than a few hundred time steps can be difficult. In this post, we will explore three tools that can allow for more efficient training of RNN models with long sequences: Optimizers, Gradient Clipping, and Batch Sequence Length.

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