Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

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

Tools to run Jupyter notebooks as jobs in Amazon SageMaker - ad hoc, on a schedule, or in response to events

Kubernetes on AWS EKS

machine learning for genomic variants

This repository contains some sample notebooks illustrating the use of DataRobot and SageMaker

Project with AWS CloudFormation templates to provision SageMaker notebooks setup to work with fast.ai

This repo contain example notebooks with instructions on using Intel AI Software listed in AWS SageMaker Marketplace.

My AWS Notebook

Example notebooks that show how to use VITech Lab SageMaker Models & Algorithms to apply machine learning and deep learning in Amazon SageMaker

Personal guide and examples to learn and use AWS SageMaker to deploy your ML model at scale.

Technical notebook articles tracking my explorations

Continually updated data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

Various Template Notebooks for Deploying ML models with Amazon Sagemaker

Schedule start/stop for your SageMaker notebook instances.

Jupyter notebooks to help team members utilize AWS SageMaker tools

SageMaker-ready jupyter notebook for Machine Learning social media digestion.

Bash Script that helps you deploy Jupyter Notebook on AWS EC2 Instance

This repository holds the python files and notebooks associated with the Udacity Data Engineering Nanodegree.

`databricks-utils` is a python package that provide several utility classes/func that improve ease-of-use in databricks notebook.

This repository contains all the assignment notebooks of the course, AWS Computer Vision: Getting Started with GluonCV.

This project shows step-by-step guide on how to build a real-world flower classifier of 102 flower types using TensorFlow, Amazon SageMaker, Docker and Python in a Jupyter Notebook.

This project is intended to use Amazon's SageMaker platform, it is assumed to have a working notebook instance which can be used to clone the deployment repository.

my notes

Generates a Vagratn box using Packer to have Java and Zeppelin installed in the correct version to use with AWS Glue development endpoint as the tutorial needs: https://docs.aws.amazon.com/glue/latest/dg/dev-endpoint-tutorial-local-notebook.html

Deploying a Sentiment Analysis Model on Amazon Sagemaker which consists of deploying a Sentiment Analysis model using Recurrent Neural Networks in the Amazon AWS SageMaker tool. The notebook and Python files provided here result in a simple web application which interacts with a deployed recurrent neural network performing sentiment analysis on movie reviews. In the final architecture AWS API Gateway and AWS Lambda functions is used as well.