## Solved – ny explanation for the spatial batch normalization

I read this part in the paper but i didn't fully understand. "we additionally want the normalization to obey the convolutional property – so that different elements of the same feature map, at different locations, are normalized in the same way" 1- what is the meaning of "convolutional property" and "normalized in the same way"? … Read more

## Solved – What does it mean to say that CNN has sparse connections

Closed. This question needs details or clarity. It is not currently accepting answers. Want to improve this question? Add details and clarify the problem by editing this post. Closed 4 years ago. Improve this question what does it mean to say that CNN has sparse connections? why CNNs are sparsely connected? Best Answer In neural … Read more

## Solved – How does backpropagation work in the case of reinforcement learning for games

If we want a neural network to learn how to recognize e.g. digits, the backpropagation procedure is as follows: Let the NN look at an image of a digit, and output its probabilities on the different digits. Calculate the gradient of the loss function w.r.t. the parameters, and adjust the parameters. But now let's say … Read more

## Solved – How does backpropagation work in the case of reinforcement learning for games

If we want a neural network to learn how to recognize e.g. digits, the backpropagation procedure is as follows: Let the NN look at an image of a digit, and output its probabilities on the different digits. Calculate the gradient of the loss function w.r.t. the parameters, and adjust the parameters. But now let's say … Read more

## Solved – How to implement a deep autoencoder

I'm trying to replicate results of this paper using Theano. The problem at the moment is, all Theano-related tutorials are only for MNIST classifiers, which isn't much use in unsupervised image retrieval. I have following idea how to approach the implementation of this problem: First, I have to train a stack of inter-connected RBMs. When … Read more

## Solved – How to implement a deep autoencoder

I'm trying to replicate results of this paper using Theano. The problem at the moment is, all Theano-related tutorials are only for MNIST classifiers, which isn't much use in unsupervised image retrieval. I have following idea how to approach the implementation of this problem: First, I have to train a stack of inter-connected RBMs. When … Read more

## Solved – Which deep learning model can classify categories which are not mutually exclusive

Examples : I have a sentence in job description : "Java senior engineer in UK ". I want to use a deep learning model to predict it as 2 categories : English and IT jobs. If i use traditional classification model, it only can predict 1 label with softmax function at last layer . Thus, … Read more

## Solved – Motivating sigmoid output units in neural networks starting with unnormalized log probabilities linear in \$z=w^Th+b\$ and \$phi(z)\$

Background: I'm studying chapter 6 of Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville. In section 6.2.2.2 (pages 182 of 183 which can be viewed here) the use of sigmoid to output \$P(y=1|x)\$ is motivated. To summarize some of the material they let \$\$z = w^Th+b\$\$ be an output neuron before an … Read more

## Solved – How to determine what type of layers do I need for the Deep learning model

Suppose that I have want to make a model that does something. Now when I search about the topic in Google or YouTube, I find many related tutorials and it seems like some clever programmer had already implemented that model with Deep learning. But how do they know that what type of layers, what type … Read more

## Solved – Why is this prediction of time series “pretty poor”

I am trying to learn how to use Neural Networks. I was reading this tutorial. After fitting a Neural Network on a Time Series using the value at \$t\$ to predict the value at \$t+1\$ the author obtains the following plot, where the blue line is the time series, the green is the prediction on … Read more