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Archives for July 2019

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How to Use Binder and Python for Reproducible Research marsja.se

Published July 28, 2019 under Python

In this post we will learn how to create a binder so that our data analysis, for instance, can be fully reproduced by other researchers. That is, in this post we will learn how to use binder for reproducible research.

Data, Research

PyTorch Transformers for state-of-the-art NLP medium.com

Published July 28, 2019 under Data Science

Hugging Face, the NLP startup behind several social AI apps and open source libraries such as PyTorch BERT, just released a new python library called PyTorch Transformers.

Transformers are a new set of techniques used to train highly performing and efficient models for performing natural language processing (NLP) and natural language understanding (NLU) tasks such as questions answering and sentiment analysis. Several of the recent techniques used to improve and advance the performance of NLP models, such as XLNet and BERT, are all based on a variation of Transformer.

NLP, PyTorch

Hands On Bayesian Statistics with Python, PyMC3 & ArviZ towardsdatascience.com

Published July 28, 2019 under Statistics

If you think Bayes’ theorem is counter-intuitive and Bayesian statistics, which builds upon Baye’s theorem, can be very hard to understand. I am with you.

There are countless reasons why we should learn Bayesian statistics, in particular, Bayesian statistics is emerging as a powerful framework to express and understand next-generation deep neural networks.

Bayesian Analysis, Pymc3, Python

The fastest way to be productive with machine learning loominus.ai

Published July 24, 2019 under Machine Learning

Advanced machine learning everyone can use. Stage data. Build models with no code. Manage models in production.

LightGBM, scikit-learn, TensorFlow, XGBoost

Data School’s top 25 pandas tricks jupyter.org

Published July 19, 2019 under Data Science

What it sounds like 🙂

Pandas, Python

Fast Implied Volatilities using Chebyshev Interpolation nag.com

Published July 13, 2019 under Quant Finance

Calculating Black-Scholes implied volatilities is a key part of financial modelling, and is not easy to do efficiently.

The benchmark in this field is the iterative method due to Peter Jaeckel (2015), though some banks have their own methods. NAG have teamed up with Dr Kathrin Glau and her colleagues from Queen Mary University of London to see whether their research in Chebyshev interpolation could be combined with NAG’s expertise in efficient computing to provide a faster way of obtaining implied volatilities. 

NAG, Numerical Methods

Build a Celebrity Look-Alike Detector with Azure’s Face Detect and Python pbpython.com

Published July 13, 2019 under Computer Vision

This article describes how to to use Microsoft Azure’s Cognitive Services Face API and python to identify, count and classify people in a picture. In addition, it will show how to use the service to compare two face images and tell if they are the same person. We will try it out with several celebrity look-alikes to see if the algorithm can tell the difference between two similar Hollywood actors. By the end of the article, you should be able to use these examples to further explore Azure’s Cognitive Services with python and incorporate them in your own projects.

Azure, Facial Recognition

Keras ImageDataGenerator and Data Augmentation pyimagesearch.com

Published July 13, 2019 under Data Science

In today’s tutorial, you will learn how to use Keras’ ImageDataGenerator class to perform data augmentation. I’ll also dispel common confusions surrounding what data augmentation is, why we use data augmentation, and what it does/does not do.

Image Processing, Keras, Python

Python Machine Learning Tutorial: Predicting Airbnb Prices dataquest.io

Published July 13, 2019 under Data Science

Machine learning is pretty undeniably the hottest topic in data science right now. It’s also the basic concept that underpins some of the most exciting areas in technology, like self-driving cars and predictive analytics. Searches for Machine Learning on Google hit an all-time-high in April of 2019, and they interest hasn’t declined much since.

Python, Tutorial

An open-source Python library for building data applications medium.com

Published July 13, 2019 under Data Science

Today the team at Elementl is proud to announce an early release of Dagster, an open-source library for building systems like ETL processes and ML pipelines. We believe they are, in reality, a single class of software system. We call them data applications.

Python

A Visual Intro to NumPy and Data Representation github.io

Published July 1, 2019 under Python

Numpy

Make your Photos Look Trippy! Build a Photo Filter From Scratch with Python medium.com

Published July 1, 2019 under Data Science

This tutorial will show you how to develop, completely from scratch, a stand-alone photo editing app to add filters to your photos using Python, Tkinter, and OpenCV!

OpenCV, Tkinter

Portable Computer Vision: TensorFlow 2.0 on a Raspberry Pi towardsdatascience.com

Published July 1, 2019 under Computer Vision

For roughly $100 USD, you can add deep learning to an embedded system or your next internet-of-things project.

Are you just getting started with machine/deep learning, TensorFlow, or Raspberry Pi? Perfect, this blog series is for you!

Data Science, Python, Raspberry Pi

Snagging Parking Spaces with Mask R-CNN and Python medium.com

Published July 1, 2019 under Computer Vision

But like in most cities, finding a parking space here is always frustrating. Spots get snapped up quickly and even if you have a dedicated parking space for yourself, it’s hard for friends to drop by since they can’t find a place to park.

My solution was to point a camera out the window and use deep learning to have my computer text me when a new parking spot opens up.

Data Science, Python

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