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Fashion MNIST with Keras and Deep Learning - PyImageSearch
In this tutorial you will learn how to train a simple Convolutional Neural Network (CNN) with Keras on the Fashion MNIST dataset, enabling you to classify fashion images and categories. The Fashion MNIST dataset is meant to be a (slightly more challenging) drop-in replacement for the (less challenging) MNIST dataset.
python  deeplearning  fashion  training  keras  neuralnetwork  neural  tutorial 
9 days ago by cyberchucktx
Simple Neural Network on MCUs – Hackster Blog
Edge computing is one of those things where you have the nails and are still looking for a hammer. In an earlier post, I wrote about Why Machine Learning on the Edge is critical. Pete Warden has also…
neural  network  microcontroller  project  machinelearning 
9 days ago by gilberto5757
gepris.dfg.de
iArt: Ein interaktives Analyse- und Retrieval-Tool zur Unterstützung von bildorientierten Forschungsprozessen
analysis  art  arthistory  germany  images  machine  munich  neural 
14 days ago by kintopp
The Unreasonable Effectiveness of Recurrent Neural Networks
Sequences. Depending on your background you might be wondering: What makes Recurrent Networks so special? A glaring limitation of Vanilla Neural Networks (and also Convolutional Networks) is that their API is too constrained: they accept a fixed-sized vector as input (e.g. an image) and produce a fixed-sized vector as output (e.g. probabilities of different classes). Not only that: These models perform this mapping using a fixed amount of computational steps (e.g. the number of layers in the model). The core reason that recurrent nets are more exciting is that they allow us to operate over sequences of vectors: Sequences in the input, the output, or in the most general case both.
recurrent  neural  networks  tutorial 
16 days ago by Husafan
GAN Dissection / GAN Paint
"The #GANpaint app works by directly activating and deactivating sets of neurons in a deep network trained to generate images. Each button on the left ("door", "brick", etc) corresponds to a set of 20 neurons. The app demonstrates that, by learning to draw, the network also learns about objects such as trees and doors and rooftops. By switching neurons directly, you can observe the structure of the visual world that the network has learned to model. "
neural  AI  art 
5 weeks ago by magnusc
Neural network organises world into concepts like we do - Technology Review
"researchers began probing a GAN’s learning mechanics by feeding it various photos of scenery—trees, grass, buildings, and sky. They wanted to see whether it would learn to organize the pixels into sensible groups without being explicitly told how.

Stunningly, over time, it did. By turning “on” and “off” various “neurons” and asking the GAN to paint what it thought, the researchers found distinct neuron clusters that had learned to represent a tree, for example. Other clusters represented grass, while still others represented walls or doors. In other words, it had managed to group tree pixels with tree pixels and door pixels with door pixels regardless of how these objects changed color from photo to photo in the training set."

Includes a link to the GANpaint app which demonstrates the process
AI  neural  art 
5 weeks ago by magnusc
ONNX
ONNX is a open format to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools and choose the combination that is best for them.
onnx  neural  network  ai  model  format  machine-learning  machine  learning  programming 
6 weeks ago by vicchow

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