The Viterbi algorithm (using the maximum likelihood decoding (MLD) algorithm) is a dynamic programming algorithm for finding the most likely sequence of hidden states – called the Viterbi path in hidden Markov models (HMM)Continue Reading
To perform experience replay we store the agent’s experiences et=(st,at,rt,st+1) Then we use a random sample of these prior actions instead of the most recent action to proceed. * This removes correlations in the observation sequence and smooths changes in the data distribution. * Iterative update adjusts Q towards targetContinue Reading
Learns a policy which tells an agent what action to take under what circumstances. Q-learning learns a policy that is optimal in the sense that it maximizes the expected value of the total reward over any and all successive steps, starting from the current state.Continue Reading
* Is a discrete time stochastic control process for decision making in situations where outcomes are partly random and partly under the control of a decision maker. * At each discrete time step, the process is in some state s, and the decision maker may choose any action a thatContinue Reading
A variational autoencoder provides a probability distribution for describing an observation/attribute in latent/hidden space.Continue Reading
The chain rule/general product rule permits the calculation of any member of the joint distribution of a set of random variables using only conditional probabilities.Continue Reading
* How software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. * The focus is finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge). * The environment is typically formulated as a Markov decision process (MDP) utilizingContinue Reading
It can not only process single data points (such as images), but also entire sequences of data (such as speech or video). For example, LSTM is applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition. Unlike standard feedforward neural networks, LSTM has feedback connections that make itContinue Reading
Gradient is a vector that is made up from a the derivatives of multi-variate function. Gradient of an Image = ∇f = [∂f/∂x,∂f/∂y] The gradient of a function is the vector direction/angle of the most rapid increase in Intensity(Getting Brighter), and the magnitude of that vector is how much itContinue Reading
Images – A table of pixels, usually of RGB(3D columns – a.k.a a tensor) – can be ravled/unrolled/flattened to a 1D longer array. Sound – long series of numbers(1D array) when digitally recorded(x = time(duration column), y = amplitude of the sound) – usually represented by frequences. Text – partsContinue Reading