/explore
|
You are here 
|
utkuufuk.com | ||
| | | | |
glowingpython.blogspot.com
Using regularization has many benefits, the most common are reduction of overfitting and solving multicollinearity issues. All of this is co... |
|
| | | | | ||
| | | | |
www.arrsingh.com
Linear Regression predicts the value of a dependent variable (y) given one or more independent variables (x1, x2, x3...xn). In this case, y is continuous - i.e. it can hold any value. In many real world problems[1], however, we often want to predict a binary value instead |
|
| | | | | ||
| | | | |
matbesancon.xyz
Learning by doing: predicting the outcome. |
|
| | | | | ||
| | | | |
blog.otoro.net
[AI summary] This article describes a project that combines genetic algorithms, NEAT (NeuroEvolution of Augmenting Topologies), and backpropagation to evolve neural networks for classification tasks. The key components include: 1) Using NEAT to evolve neural networks with various activation functions, 2) Applying backpropagation to optimize the weights of these networks, and 3) Visualizing the results of the evolved networks on different datasets (e.g., XOR, two circles, spiral). The project also includes a web-based demo where users can interact with the system, adjust parameters, and observe the evolution process. The author explores how the genetic algorithm can discover useful features (like squaring inputs) without human intervention, and discusses the ... |
|
| | | |||
