I understand that bias are required in small networks, to shift the activation function. But in the case of Deep network that has multiple layers of CNN, pooling, dropout and other non -linear acti...

I have been following Andrew NG's videos on neural networks. In these videos, he doesn't associate a bias to each and every neuron. Instead, he adds a bias unit at the head of every layer after the...

I am currently building a CNN in tensorflow and I am initialising my weight matrix using a He normal weight initialisation. However, I am unsure how I should initialise my bias values. I am using R...

I can make a neural network, I just need a clarification on bias implementation. Which way is better: Implement the Bias matrices B1, B2, .. Bn for each layer in their own, seperate matrix from the...

I am looking to implement a generic neural network, with 1 input layer consisting of input nodes, 1 output layer consisting of output nodes, and N hidden layers consisting of hidden nodes. Nodes ar...

I'm building CNN + Ensemble model for classify images with Tensorflow at Python. I crawled dog and cat images at google images. Then changed them to 126 * 126 pixel size and gray scale, add label 0...

A code I'm modifying is using tf.get_variable for weight variables, and tf.Variable for bias initialisation. After some searching, it seems that get_variable should always be favoured due to its po...

Isn't it true that if a bias is not present, a line passing through origin should be able to linearly separate the two data sets?? But the most popular answer in this -->> question says y ^ | -...

I posted a question yesterday regarding issues that I was having with my backpropagating neural network for the XOR operator. I did a little more work and realized that it may have to do with not h...

I was wondering: in a multi-layer feed-forward neural network should the input layer include a bias neuron, or this is just useful in hidden layers? If so, why?

I want to make a model which predicts the future response of the input signal, the architecture of my network is [3, 5, 1]: 3 inputs, 5 neurons in the hidden layer, and 1 neuron in output layer...