A perceptron is always feedforward, that is, all the arrows are going in the
direction of the output. Neural networks in general might have loops, and if
so, are often called recurrent networks. A recurrent network is much harder
to train than a feedforward network.
In addition, it is assumed that in a perceptron, all the arrows are going from
layer $i$ ...
A half-space is said to be homogeneous if the hyperplane that defines it contains the origin.
(Source: S. Vempala, A Random Sampling based Algorithm for Learning Intersections of Half-spaces. Journal of the ACM 57(6) article 32, 2010. PDF.)
Why does this help? Part of the reason is because of properties of the activation function.
Typically, most activation functions have their most interesting behavior around 0. For instance, the ReLu activation function switches from $f(x)=0$ to $f(x)=x$ at $x=0$. The sigmoid activation function has most of its interesting behavior at $x=0$, and plateaus ...
There are two parts to your question that need to be addressed.
What transformation are you performing?
Why does that help learning?
The transformation of taking your input and turning them into "Z-scores" is really just centering and standardizing the variance for each variable. The reason I put "Z-score" in quotes is because there is no ...
Personally, I've seen "multilayer neural network" used more commonly than "multilayer perceptron". If you like the former term better, use it.
That said: multilayer perceptron is an accepted term of art. Your criticism is not unreasonable, but it is what it is. Language exists to help us communicate... so pick terms that will help you communicate your ...
Yes, logistic regression can be viewed as a simple neural network with multiple inputs, a single neuron, a single output, and using the logistic function as the activation function.
No, linear regression is not a form of a neural network (a neural network would have a activation function; but there's nothing like that present in linear regression).
In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers.
Its much complex than just drawing some graph and fitting a line to divide these data. I should tell you that you gone entirely wrong about the core concept.
Our brain are capable of solving complex problem, few of which are impossible for a computer to ...
When do we say that a artificial neural network is a multilayer
Artificial neural network, which has input layer, output layer, and two or more trainable weight layers (constisting of Perceptrons) is called multilayer perceptron or MLP.
And when do we say that a artificial neural network is a multilayer?
You can say it is a multilayer ...
No, that's just not possible.
Although the perceptron initially seemed promising, it was quickly proved that perceptrons could not be trained to recognise many classes of patterns.
Check the Wikipedia article on the subject.
I think you're confusing yourself by worrying about a worst case that likely won't happen. Assuming each element of the stream comes randomly and independently from some distribution, then it is exponentially unlikely that the case you mention happens.
How do you train from a stream? Easy: each time you see an example, you update the weights of the neural ...
Your premise is wrong. A single-layer neural network (perceptron) can use a (nonlinear) activation function. Nothing prevents you from doing that.
It's common that the last layer of a neural network doesn't use any activation function, and instead is input into a softmax layer. If there's only one layer, that means that no activation function is used. ...
I suspect the book's English explanation is probably a simplification (or maybe it has adopted the standard convention of adding an extra dimension to x that holds a constant value $1$). It seems like most likely you've already given the explanation; it's adding the bias value.
In the context of neural networks, a perceptron is an artificial
neuron using the Heaviside step function as the activation function.
So the perceptron is a special type of a unit or a neuron. Hence multilayer perceptron is a subset of multilayer neural networks.