aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filte…

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Reference models and tools for Cloud TPUs.

Plain python implementations of basic machine learning algorithms

Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters

Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier

PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different scenarios.

Code for the Lovász-Softmax loss (CVPR 2018)

Distributed deep learning on Hadoop and Spark clusters.

Evaluation of the CNN design choices performance on ImageNet-2012.

This library augments road images to introduce various real world scenarios that pose challenges for training neural networks of Autonomous vehicles. Automold is created to train CNNs in specific w…

Detectorch - detectron for PyTorch

Observations and notes to understand the workings of neural network models and other thought experiments using Tensorflow

Gumbel-Softmax Variational Autoencoder with Keras

To train deep convolutional neural networks, the input data and the activations need to be kept in memory. Given the limited memory available in current GPUs, this limits the maximum dimensions of …

Blog for the Open Institute for Advanced Study