AI News, aigamedev/scikit-neuralnetwork
- On Thursday, August 23, 2018
- By Read More
By importing the sknn package provided by this library, you can easily train deep neural networks as regressors (to estimate continuous outputs from inputs) and classifiers (to predict discrete labels from features).
Thanks to the underlying Lasagne implementation, the code supports the following neural network features — exposed in an intuitive and well documented API: If a feature you need is missing, consider opening a GitHub Issue with a detailed explanation about the use case and we'll see what we can do.
Then, you can run the samples and benchmarks available in the examples/ folder, or launch the tests to check everything is working: We strive to maintain 100% test coverage for all code-paths, to ensure that rapid changes in the underlying backend libraries are caught automatically.
To run the example that generates the visualization above using our sknn.mlp.Classifier, just run the following command in the project's root folder: There are multiple parameters you can plot as well, for example iterations, rules or units.
- On Monday, February 17, 2020
Eben Olson: Neural networks with Theano and Lasagne
PyData NYC 2015 An introduction to neural networks using the Theano computational library and the Lasagne framework. After introducing the basics of ...
Deep Learning: Keras Short Tutorial
Deep Learning, Theano Short Tutorial
Convolution Neural Network in Keras (Understanding through MNIST data set)
This video shows how to implement a Convolution neural network in keras library. The network trains and tests on the famous MNIST data set and it runs on the ...
Introduction to Deep Learning with Python
Alec Radford, Head of Research at indico Data Solutions, speaking on deep learning with Python and the Theano library. The emphasis of the talk ..