Mario O. Bourgoin

marabout2015/AIReferenceArchitectures

Official Azure Reference Architectures for AI workloads

marabout2015/gpt-engineer

Specify what you want it to build, the AI asks for clarification, and then builds it.

marabout2015/docker-images

Repository of Docker images created by Continuum Analytics

marabout2015/LangchainDocuments

marabout2015/mmlspark

Microsoft Machine Learning for Apache Spark

marabout2015/recommenders

Best Practices on Recommendation Systems

marabout2015/multivariate-time-series-data

marabout2015/AKSDeploymentTutorial

Tutorial on how to deploy Deep Learning models on GPU enabled Kubernetes cluster

marabout2015/bert_language_understanding

Pre-training of Deep Bidirectional Transformers for Language Understanding: pre-train TextCNN

marabout2015/mixed-reality

marabout2015/bert-chainer

Chainer implementation of "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding"

marabout2015/bert

TensorFlow code and pre-trained models for BERT

marabout2015/pytorch-pretrained-BERT

A PyTorch implementation of Google AI's BERT model provided with Google's pre-trained models, examples and utilities.

marabout2015/azure-docs

marabout2015/TextWorld

​TextWorld is a sandbox learning environment for the training and evaluation of reinforcement learning (RL) agents on text-based games.

marabout2015/bert-as-service

Mapping a variable-length sentence to a fixed-length vector using BERT model

marabout2015/mt-dnn

Multi-Task Deep Neural Networks for Natural Language Understanding

marabout2015/tpu

Reference models and tools for Cloud TPUs.

marabout2015/EconML

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

marabout2015/Advanced-Deep-Learning-with-Keras

Advanced Deep Learning with Keras, published by Packt

marabout2015/handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

marabout2015/codebase

Useful scripts

marabout2015/PeoplePlacesThings

Use AR headset to assist elderly or disabled. Recognize objects/people and provide verbal labels & speech to denote what it is. Also incorporate translations.

marabout2015/yfinance

Yahoo! Finance market data downloader (+faster Pandas Datareader)

marabout2015/course-v3

The 3rd edition of course.fast.ai

marabout2015/ray

A fast and simple framework for building and running distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

marabout2015/handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

marabout2015/AzureML-BERT

End-to-end walk through for fine-tuning BERT using Azure Machine Learning