/explore
|
You are here 
|
jingnanshi.com | ||
| | | | |
comsci.blog
|
|
| | | | | In this tutorial, we will learn two different methods to implement neural networks from scratch using Python: Extremely simple method: Finite difference Still a very simple method: Backpropagation | |
| | | | |
blog.demofox.org
|
|
| | | | | This article explains how these four things fit together and shows some examples of what they are used for. Derivatives Derivatives are the most fundamental concept in calculus. If you have a function, a derivative tells you how much that function changes at each point. If we start with the function $latex y=x^2-6x+13$, we can... | |
| | | | |
liorsinai.github.io
|
|
| | | | | A series on automatic differentiation in Julia. Part 1 provides an overview and defines explicit chain rules. | |
| | | | |
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 ... | ||
