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honto

Extracting Concept Hierarchies from Textbooks

improving-forecast-accuracy-with-machine-learning

The Improving Forecast Accuracy with Machine Learning solution generates, tests, compares, and iterates on Amazon Forecast forecasts. The solution automatically produces forecasts and generates visualization dashboards for Amazon QuickSight or Amazon SageMaker Jupyter Notebooks—providing a quick, easy, drag-and-drop interface that displays time series input and forecasted output.

incubator-pinot

Apache Pinot (Incubating) - A realtime distributed OLAP datastore

interactive-coding-challenges

120+ interactive Python coding interview challenges (algorithms and data structures). Includes Anki flashcards.

intro2stats

Introduction to Statistics using Python

junk

:notebook: mooc code playground

kafka-streams-examples

Demo applications and code examples for Apache Kafka's Streams API.

kaggle-toxic-comments

Ensemble stacking using Keras / Tensorflow. Used LSTM RNN, Logistic Regression & XGB Classifier for first level, and simple CNN for metalearning.

langchain

⚡ Building applications with LLMs through composability ⚡

langchain-aws-template

Application template for service api using langchain and generative model services

leetcode

:pencil: Python / C++ 11 Solutions of All 468 LeetCode Questions

leetcode-1

Python solutions of LeetCode questions.

legal-text-analytics

A list of selected resources, methods, and tools dedicated to Legal Text Analytics.

lexpredict-contraxsuite

LexPredict ContraxSuite

lstm-neural-network-for-time-series-prediction

LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data

luigi_gdb_pipeline_demo

An example to illustrate using Luigi to manage a data science workflow in Greenplum Database

machine-learning-engineer-nanodegree

Machine Learning Engineer Nanodegree Projects

machine-learning-explorer-in-shiny

machine-learning-for-telecommunications

A base solution that helps to generate insights from their data. The solution provides a framework for an end-to-end machine learning process including ad-hoc data exploration, data processing and feature engineering, and modeling training and evaluation. This baseline will provide the foundation for industry specific data to be applied and models created to release industry specific ML solutions.

machine-learning-on-aws

Machine Learning on AWS using various methods/examples

machine-learning-systems-design

A booklet on machine learning systems design with exercises

machlearn

Machine learning dashboard created with R/shiny

mit-data-science

ml-prod

Some thoughts on how to use machine learning in production

mlops-amazon-sagemaker-devops-with-ml

Workshop content for applying DevOps practices to Machine Learning workloads using Amazon SageMaker

mlwatcher

mysql-5.6

Facebook's branch of the Oracle MySQL v5.6 database. This includes MyRocks.

neo-ai-dlr

Neo-AI-DLR is a common runtime for machine learning models compiled by AWS SageMaker Neo, TVM, or TreeLite.

neo4j-mazerunner

Mazerunner extends a Neo4j graph database to run scheduled big data graph compute algorithms at scale with HDFS and Apache Spark.

neuroevolution-robots

Neuroevolution demo through TensorFlow.js, Neataptic, and Box2D