There was a computer vision challenge that was hosted at kaggle.com about a year ago named IEEE’s Signal Processing Society — Camera Model…

Visualizer for neural network, deep learning and machine learning models - lutzroeder/netron

Let’s imagine it’s a fine afternoon and you have written even a finer algorithm for your model and you expect that it will give you…

Training a neural network with a small dataset can cause the network to memorize all training examples, in turn leading to overfitting and poor performance on a holdout dataset. Small datasets may also represent a harder mapping problem for neural networks…

For the past month, we ranked nearly 1,400 Machine Learning articles to pick the Top 10 stories that can help advance your career (0.7%…

To make it easier to build and deploy natural language processing (NLP) systems, we are open-sourcing PyText, a modeling framework that blurs the boundaries between experimentation and large-scale …

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2021 Update: I created this brief and highly accessible…

Recently I found a paper being presented at NeurIPS this year, ennoscriptd Neural Ordinary Differential Equations, written by Ricky Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud from the University of Toronto. The core idea is that certain types…

Deep Learning has changed the entire landscape over the past few years and its results are steadily improving. This article presents some of the main advances and accomplishments in Deep Learning for 2018.

Нижегородский офис компании Intel, помимо прочего, занимается разработкой алгоритмов компьютерного зрения на основе глубоких нейронных сетей. Многие наши алгорит...

Generating style-specific text from a small corpus of 2.5k sentences using a pre-trained language model. Code in PyTorch

Ensemble learning are methods that combine the predictions from multiple models.

It is important in ensemble learning that the models that comprise the ensemble are good, making different prediction errors. Predictions that are good in different ways can…

Teaching page of Shervine Amidi, Graduate Student at Stanford University.

David Duvenaud was collaborating on a project involving medical data when he ran up against a major shortcoming in AI. An AI researcher at the University of Toronto, he wanted to build a deep-learning model that would predict a patient’s health over time.…

NVIDIA’s novel architecture for Generative Adversarial Networks