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2018-cs109b

Harvard CS109b Public Repository

a-2017

Public Repository for cs109a, 2017 edition

advanced_python_public

ai_tour

Tutorials presented at the Microsoft AI+ Tour 2019

aiforearth-api-development

The AI for Earth Engineering Team works to empower AI for Earth Grantees by providing base images to quickly move from a local model to an model-backed API that any permitted user can utilize.

amazon-sagemaker-deepar-retail

Use SageMaker's DeepAR algorithm to continuously develop store inventory forecasts in response to changing data.

amazon-sagemaker-developer-guide

The open source version of the Amazon SageMaker docs

amazon-sagemaker-examples

Example notebooks that show how to apply machine learning and deep learning in Amazon SageMaker

automl-mlads2019

A guide for the automated ML MLADS 2019 workshop to be used in conjunction with the sample notebooks

awesome-courses

:books: List of awesome university courses for learning Computer Science!

azure-kusto-analytics-lib

Azure Data Explorer advanced analytics library (functions, queries, etc.)

cloths_segmentation

Code for binary segmentation of cloths

deep-learning-coursera

Deep Learning Specialization by Andrew Ng on Coursera.

deeplearning-models

A collection of various deep learning architectures, models, and tips

dlfd

Deep Learning For Designers Workshop Notebooks and Files

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.

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.

hes_csci_e-82

hyperdrivedeeplearning

Hyperparameter Tuning for Deep Learning

interpretability

Interpretability Techniques to Build Robust AI Applications

k-nearest-neighbors-with-dynamic-time-warping

Python implementation of KNN and DTW classification algorithm

kaggle-web-traffic

1st place solution

kmodes

Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data

lession-detection-with-yolo

m4-methods

Includes the source code of the methods which participated in the M4 Competition

machinelearningnotebooks

Python notebooks with ML and deep learning examples with Azure Machine Learning | Microsoft

mlads-aml

A Lap Around Azure Machine Learning

mlads19_dl4nlp

MLADS Fall 2019: Deep Learning for NLP Tutorial

mlads_rapids

Public repository for Microsoft AzureML and NVIDIA RAPIDS workshop at MLADS

mlaksdeployaml

Repository for deploying machine learning models using Azure Machine Learning