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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.

2 votes
1 answer
4k views

MSE and different types of activation functions in NN

Lets say I have 3 neurons in the last layer of my neural network and I am using mean squared error as a loss function. The desired output of my neural network is a vector: [false,true,false] If an ac …
koryakinp's user avatar
  • 219
2 votes
0 answers
1k views

Reinforcement Learning: A2C agent does not learn

I am trying to implement an A2C algorithm, but for some reasons, my agent does not learn very well. I build a custom environment using Unity ML Agents. The environment is very simple: an agent can co …
koryakinp's user avatar
  • 219
1 vote
1 answer
90 views

Validate implementation of back-propagation algorithm

Let's say I implemented a CNN. Is there an easy way I can validate, that my implementation of back-propagation does not contain errors ? May be I can feed some dummy values into my network so it can …
koryakinp's user avatar
  • 219
0 votes
1 answer
104 views

Cross Entopy Loss for classification

Suppose I have a neural network, which classifies pictures of cats, dogs and fishes. The neural network uses Softmax as an activation function of the output layer. Let's say I feed a picture of dog a …
koryakinp's user avatar
  • 219