Contents
What does a neural network compute?
neural network, a computer program that operates in a manner inspired by the natural neural network in the brain. The objective of such artificial neural networks is to perform such cognitive functions as problem solving and machine learning.
What can neural networks solve?
Today, neural networks are used for solving many business problems such as sales forecasting, customer research, data validation, and risk management. For example, at Statsbot we apply neural networks for time-series predictions, anomaly detection in data, and natural language understanding.
Can neural network solve regression?
Neural networks are flexible and can be used for both classification and regression. In this article, we will see how neural networks can be applied to regression problems. Regression models work well only when the regression equation is a good fit for the data. Most regression models will not fit the data perfectly.
Are neural networks supervised or unsupervised?
The learning algorithm of a neural network can either be supervised or unsupervised. A neural net is said to learn supervised, if the desired output is already known. While learning, one of the input patterns is given to the net’s input layer.
Which is larger y1 or y2 in a neural network?
If the weight connected to the X1 neuron is much larger than the weight connected to the X2 neuron the the error on Y1 is much more influenced by X1 since Y1 = ( X1 * W11 + X2 * X12). So if W11 is larger than W12 we should pass more of the Y1 error to the X1 neuron since this is the neuron that contributes to it.
How to write equations for a neural network?
In this notation the first index of the of the weight indicates the output neuron and the second index indicates the input neuron, so for example W12 is weight on connection from X2 to Y1. Now we can write the equations for Y1 and Y2: Now this equation can be expressed using matrix multiplication.
When do you replace X with Y in a neural network?
If X equals 2, then Y also equals 2. If X is 4, Y also is 4. So what we need is a function that accepts a single input X and returns a single output. This output is identical to the input. With no doubt, the function is F (X)=X. For simplicity, we can replace F (X) by Y. So, the function will be Y=X.
How are neurons connected in a neural network?
Neuron Y1 is connected to neurons X1 and X2 with weights W11 and W12 and neuron Y2 is connected to neurons X1 and X2 with weights W21 and W22. In this notation the first index of the of the weight indicates the output neuron and the second index indicates the input neuron, so for example W12 is weight on connection from X2 to Y1.