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Artificial neural networks (ANNs) are a broad class of computational models loosely based on biological neural networks. They encompass feedforward NNs (including "deep" NNs), convolutional NNs, recurrent NNs, etc.

Suppose I have the following architecture Where $HA_i$ and $OA_i$ are the activated values of the hidden and output nodes respectively, and $W_i$ are the weights between nodes. I want to find …
asked Nov 10 '17 by Edv Beq
Given a simple data set to train with neural networks where i.e.: wine quality is the categorical output and measurements of acidity, sugar, etc. are the numerical inputs. The output can be written …
asked Jan 11 by Edv Beq
In Neural Network examples that I have seen online - sometimes the Mean Square Error is presented as $$MSE = \frac{{1}}{2n} \sum_{i}^{n} ( \widehat{y_i} -y_i)^2 \quad (1)$$ and other times $$… asked Nov 10 '17 by Edv Beq 2answers Consider the following problem involving neural networks. The input of the neural network are n paths of a diffusion model i.e.:  dX(t)=\mu dt + \sigma dW(t) , at some random time t.$$ input = …
asked Jul 9 '18 by Edv Beq
Let $\mathbb{E}_x[g(X_t)]$ be the expected value of a random variable $X_t$ with known probability density $f_t(x)$ then for the continuous case \mathbb{E}[g(X_t)] = \int_{-\infty}^{\infty} g(x)f …
How many times do we sample from $Q(z|x)$ in a Variational Autoencoder? Let’s say that the autoencoder input $x$ is a single image 28x28 pixels - and $Z$ is is a one dimensional distribution. Then, t …
I have simulated the relative frequency of a stochastic process by creating a very small grid say $1000$ by $1000$. The graph looks like this Now I am trying to setup a regression model by matc …