A python code for acquisition of EMG signal and classification through an Artificial Neural Network - IgorMunizS/emg-control-prothesys

In this tutorial, you will learn how to perform anomaly/novelty detection in image datasets using OpenCV, Computer Vision, and the scikit-learn machine learning library.

In this tutorial you will learn the basics of augmented reality with OpenCV and Python.

ICA applied to EEG part 1: What is ICA? This video describes what Independent Component Analysis is and how it can be applied to EEG data.

🏆 SOTA for Image Generation on FFHQ 512 x 512 (FID metric)

Generating realistic looking faces with GANs network - PooyaAlamirpour/FaceGeneration

In this Jupyter Notebook it will be explained how some parameters can be extracted from EMG, both from time and frequency domain.

In this blog post, I will discuss the GANs and how it is used for restoring the images. I will use the fastai library for the practical…

Seizure prediction from EEG data using machine learning. 3rd place solution for Kaggle/Uni Melbourne seizure prediction competition. - garethjns/Kaggle-EEG

In this video, we will create a face filter using OpenCV. We will first detect the face and then its landmarks which will allow us to extract individual features such as eyes nose lips and more. From there we will overlay our desired color to the lip feature.…

CapsNets are a hot new architecture for neural networks, invented by Geoffrey Hinton, one of the godfathers of deep learning.

NIPS 2017 Paper:
* Dynamic Routing Between Capsules,
* by Sara Sabour, Nicholas Frosst, Geoffrey E. Hinton
* https://arxiv.org/abs/1710.09829…

In this short video, Max Margenot gives an overview of supervised and unsupervised machine learning tools. He covers regression and classification, canonical problems in supervised learning. Max gives an intuitive denoscription of the differences between these…

Principal Component Analysis, is one of the most useful data analysis and machine learning methods out there. It can be used to identify patterns in highly complex datasets and it can tell you what variables in your data are the most important. Lastly, it…

This brain anatomy segmentation dataset has 1300 2D US scans for training and 329 for testing. A total of 1629 in vivo B-mode US images were obtained from 20 different subjects (age<1 years old) who were treated between 2010 and 2016. The dataset contained…

Implemented in one code library.

Welcome to GANs for Good- a virtual expert panel hosted by DeepLearning.AI!

To celebrate the launch of GANs Specialization, we’ve assembled a panel of GANs experts. They will discuss some of their current projects and the importance and future of GANs and…