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Why is a bias parameter needed in neural networks?

It's not strictly "needed." In fact, if you look at things like Keras, you will see that layers have a use_bias parameter, which defaults to True, but ...

What exactly is averaged when doing batch gradient descent?

Introduction First of all, it's completely normal that you are confused because nobody really explains this well and accurately enough. Here's my partial attempt to do that. So, this answer doesn't ...
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What is the time complexity of the forward pass algorithm of a feedforward neural network?

Let's suppose that we have an MLP with $15$ inputs, $20$ hidden neurons and $2$ output neurons. The operations performed are only in the hidden and output neurons, given that the input neurons only ...
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What is a recurrent neural network?

A recurrent neural network (RNN) is an artificial neural network that contains backward or self-connections, as opposed to just having forward connections, like in a feed-forward neural network (FFNN)....
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Why is a bias parameter needed in neural networks?

Let's write some code, shall we? First I'll generate two 2D Gaussian blobs with means at (0,0) and at (3,3) and sigma = 1.0. The points for the blob at (0,0) will be in class ...
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What is a recurrent neural network?

Recurrent neural networks (RNNs) are a class of artificial neural network architecture inspired by the cyclical connectivity of neurons in the brain. It uses iterative function loops to store ...
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How to express a fully connected neural network succintly using linear algebra?

The equation $$\hat{y} = \sigma(xW_\color{green}{1})W_\color{blue}{2} \tag{1}\label{1}$$ is the equation of the forward pass of a single-hidden layer fully connected and feedforward neural network, i....
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Why is a bias parameter needed in neural networks?

If you have data generated from $y = 5\,x + 3$ How do you expect the simplest neural net $y = w_1 x$ to adjust the data ? That is why $b$ is useful.
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Why is a bias parameter needed in neural networks?

No matter what you make $W_1$ and $W_2$, if $X_1$ is 0 and $X_2$ is 0 then $W_1X_1+W_2X_2$ is 0 which (in a typical classification application) means the classifier is completely unsure which class it ...

Why use a recurrent neural network over a feedforward neural network for sequence prediction?

An RNN or LSTM have the advantage of "remembering" the past inputs, to improve performance over prediction of a time-series data. If you use a neural network over like the past 500 characters, this ...
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How do I decide the optimal number of layers for a neural network?

There is a technique called Pruning in neural networks, which is used just for this same purpose. The pruning is done on the number of hidden layers. The process ...
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Can the hidden layer prior to the ouput layer have less hidden units than the output layer?

A layer with bigger number of nodes than previous one is something very common. Some examples are: strategies encoder-decoder (autoencoders) where the encoder typically has layers with a decreasing ...
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