|
You are here |
thecodebarbarian.com | ||
| | | | |
blog.ephorie.de
|
|
| | | | | [AI summary] The blog post explores the connection between logistic regression and neural networks, demonstrating how logistic regression can be viewed as the simplest form of a neural network through mathematical equivalence and practical examples. | |
| | | | |
golb.hplar.ch
|
|
| | | | | [AI summary] The article describes the implementation of a neural network in Java and JavaScript for digit recognition using the MNIST dataset, covering forward and backpropagation processes. | |
| | | | |
coornail.net
|
|
| | | | | Neural networks are a powerful tool in machine learning that can be trained to perform a wide range of tasks, from image classification to natural language processing. In this blog post, well explore how to teach a neural network to add together two numbers. You can also think about this article as a tutorial for tensorflow. | |
| | | | |
blog.otoro.net
|
|
| | | [AI summary] This text discusses the development of a system for generating large images from latent vectors, combining Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). It explores the use of Conditional Perceptual Neural Networks (CPPNs) to create images with specific characteristics, such as style and orientation, by manipulating latent vectors. The text also covers the ability to perform arithmetic on latent vectors to generate new images and the potential for creating animations by transitioning between different latent states. The author suggests future research directions, including training on more complex datasets and exploring alternative training objectives beyond Maximum Likelihood. | ||