/explore

Click through on any links that interest you or select the planets on the right to continue exploring the Outer Web.
You are here

 wtfleming.github.io
Cats vs Dogs - Part 1 - 92.8% Accuracy - Binary Image Classification with Keras and Deep Learning
| | glowingpython.blogspot.com
The Glowing Python: Solving the Two Spirals problem with Keras
1.7 parsecs away

Travel
| | In this post we will see how to create a Multi Layer Perceptron (MLP), one of the most common Neural Network architectures, with Keras. Then...
| | www.secondstate.io
AI as a Service: Image Classification Using MobileNet
2.3 parsecs away

Travel
| | [AI summary] This article demonstrates how to build and deploy an image classification service using MobileNet, Rust, and WebAssembly on a Function as a Service platform.
| | web.navan.dev
Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria
1.2 parsecs away

Travel
| | Tutorial on creating an image classifier model using TensorFlow which detects malaria
| | blog.otoro.net
Neural Network Evolution Playground with Backprop NEAT |
11.5 parsecs away

Travel
| [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 ...