Marton Trencseni - Sat 01 June 2019 • Tagged with python, pytorch, cnn, torchvision, mnist, skl
It’s easy to build a CNN that does well on MNIST digit classification. How easy is it to break it, to distort the images and cause the model to misclassify?
Marton Trencseni - Tue 14 May 2019 • Tagged with python, pytorch, cnn, torchvision, cifar, skl
CIFAR-10 is a classic image recognition problem, consisting of 60,000 32x32 pixel RGB images (50,000 for training and 10,000 for testing) in 10 categories: plane, car, bird, cat, deer, dog, frog, horse, ship, truck. Convolutional Neural Networks (CNN) do really well on CIFAR-10, achieving 99%+ accuracy. The Pytorch distribution includes an example CNN for solving CIFAR-10, at 45% accuracy. I will use that and merge it with a Tensorflow example implementation to achieve 75%. We use torchvision to avoid downloading and data wrangling the datasets. Like in the MNIST example, I use Scikit-Learn to calculate goodness metrics and plots.
Marton Trencseni - Thu 02 May 2019 • Tagged with python, pytorch, cnn, torchvision, mnist, skl
MNIST is a classic image recognition problem, specifically digit recognition. It contains 70,000 28x28 pixel grayscale images of hand-written, labeled images, 60,000 for training and 10,000 for testing. Convolutional Neural Networks (CNN) do really well on MNIST, achieving 99%+ accuracy. The Pytorch distribution includes a 4-layer CNN for solving MNIST. Here I will unpack and go through this example. We use torchvision to avoid downloading and data wrangling the datasets. Finally, instead of calculating performance metrics of the model by hand, I will extract results in a format so we can use SciKit-Learn's rich library of metrics.
Marton Trencseni - Tue 16 April 2019 • Tagged with pytorch, svm, iris
I use the standard Iris dataset for supervised learning with a Support Vector Machine model using Pytorch's autograd.
Marton Trencseni - Tue 12 March 2019 • Tagged with pytorch, embedding
A PyTorch model is trained on public Hacker News data, embedding posts and comments into a high-dimensional vector space, using the mean squared error (MSE) of dot products as the loss function. The resulting model is reasonably good at finding similar posts and recommending posts for users.
Marton Trencseni - Fri 08 February 2019 • Tagged with pytorch
I will show how to solve the standard A x = b matrix equation with PyTorch. This is a good toy problem to show some guts of the framework without involving neural networks.
Marton Trencseni - Sun 27 January 2019 • Tagged with fetchr, machine-learning, call-center
Over a period of 6 months, we rolled out a Machine Learning model to predict a customer’s delivery (latitude, longitude). During the recent holiday peak, this ML model handled most of Fetchr’s order scheduling.