sl7rf Goto Github PK

aws-big-data-blog

bookclub

Notes and links from the book club meetings

chinese-poetry

Chinese poetry generation and deploy on Lambda/API Gateway

config

all config about bash/vim/alias etc.

darkflow

Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

darknet_scripts

Auxilary scripts to work with (YOLO) darknet deep learning famework. AKA -> How to generate YOLO anchors?

datacache

Package to maintain a data cache.

deep-learning-coursera

Deep Learning Specialization by Andrew Ng on Coursera.

detroit-blight-analysis

examples

TensorFlow examples

hello-github-actions

kaggle

Containing codes of participation in Kaggle competitions.

kaggle-2014-criteo

kaggle-ndsb

Winning solution for the National Data Science Bowl competition on Kaggle (plankton classification)

kaggle-telstra

My code for Telstra Network Disruptions Kaggle competition

large-scale-machine-learning-with-python

Code repository for Large Scale Machine Learning with Python, published by Packt

learningobjectdetection

lightgbm

A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.

machine-learning-in-r

This repository contains my ML scripts in R

minibook-2nd-code

Code of the IPython Minibook, 2nd edition (2015)

odsc_mlops_from_model_to_prod

Machine Learning Operations (MLOps) are essential to build successful Data Science use-cases. Today, ML is powering data driven use-cases that are transforming industries around the world. In order to seize and hold it's competitive advantage business needs to reduce risk therefore a new expertise rises to include data science models in operational systems. According to Gartner Research “While many organizations have experimented with AI proofs of concept, there are still major blockers to operationalizing its development. IT leaders must strive to move beyond the POC to ensure that more projects get to production and that they do so at scale to deliver business value. (July 2020)”. In this session, we will discuss the role of MLOps and how they can help data science models from deployment to maintenance with focus on: keep track of performance degradation overtime from model predictions quality, setting up continuous evaluation metrics and tuning the model performance in both training and serving pipelines that are deployed in production.

pipeliner

Machine learning pipelines for R.

product-nets

This is the tensorflow implementation of "Product-based Neural Networks for User Response Prediction".

pykrige

Kriging Toolkit for Python

pykriging

Welcome to the User Friendly Python Kriging Toolbox!

python-gui-examples

A growing collection of bare-bones GUI examples for python (PyQt4, Python 3)

rake-tutorial

A python implementation of the Rapid Automatic Keyword Extraction

scipy_con_2019

Tutorial Sessions for SciPy Con 2019

sds385

SDS 385: Statistical Models for Big Data

sigopt

Solvers for sigmoidal programming problems