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Resources for developers using Python for scientific computing and quantitative analysis

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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

Keras Mask R-CNN pyimagesearch.com

Published June 18, 2019 under Machine Learning

In this tutorial, you will learn how to use Keras and Mask R-CNN to perform instance segmentation (both with and without a GPU).

Using Mask R-CNN we can perform both: Object detection, giving us the (x, y)-bounding box coordinates of for each object in an image; Instance segmentation, enabling us to obtain a pixel-wise mask for each individual object in an image.

Computer Vision, Convolutional NN, Keras

Artificial Intelligence Made Easy with H2O.ai towardsdatascience.com

Published June 18, 2019 under Machine Learning

A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python

AutoML, H2O.ai, Python

Fine-tuning with Keras and Deep Learning pyimagesearch.com

Published June 10, 2019 under Machine Learning

In this tutorial, you will learn how to perform fine-tuning with Keras and Deep Learning.

We will take a CNN pre-trained on the ImageNet dataset and fine-tune it to perform image classification and recognize classes it was never trained on.

Today is the final post in our three-part series on fine-tuning:

  1. Part #1: Transfer learning with Keras and Deep Learning
  2. Part #2: Feature extraction with on large datasets with Keras and Deep Learning
  3. Part #3: Fine-tuning with Keras and Deep Learning (today’s post)

Computer Vision, Keras

CNNs, Part 2: Training a Convolutional Neural Network victorzhou.com

Published June 10, 2019 under Machine Learning

In this post, we’re going to do a deep-dive on something most introductions to Convolutional Neural Networks (CNNs) lack: how to train a CNN, including deriving gradients, implementing backprop from scratch (using only numpy), and ultimately building a full training pipeline!

Convolutional NN, Deep Learning

A high-level app and dashboarding solution for Python pyviz.org

Published June 10, 2019 under Data Science

Panel is an open-source Python library that lets you create custom interactive web apps and dashboards by connecting user-defined widgets to plots, images, tables, or text.

Data Visualization, Open Source, PyData

How to make the most amount of money with the least amount of risk? towardsdatascience.com

Published June 3, 2019 under Quant Finance

In this article, we show one such amazing application of LP using Python programming in the area of economic planning — maximizing the expected profit from a stock market investment portfolio while minimizing the risk associated with it.

CVXPY, Economics, MPT, Optimization

Image Recognition in Python with TensorFlow and Keras

Published June 3, 2019 under Machine Learning

One of the most common utilizations of TensorFlow and Keras is the recognition/classification of images. If you want to learn how to use Keras to classify or recognize images, this article will teach you how.

Keras, TensorFlow

Text Analysis in Python to Test a Hypothesis dataquest.io

Published June 3, 2019 under Machine Learning

People often complain about important subjects being covered too little in the news. One such subject is climate change. The scientific consensus is that this is an important problem, and it stands to reason that the more people are aware of it, the better our chances may be of solving it. But how can we assess how widely covered climate change is by various media outlets? We can use Python to do some text analysis!

NLP, Python

How We Built a Content-Based Filtering Recommender System For Music with Python towardsdatascience.com

Published June 3, 2019 under Data Science

This project refers to Lambda Labs at Lambda School in which students spent the past 5 weeks building production-grade web applications, with some of them utilizing machine learning models as part of their backends.

Lambda, Recommenders

Data science best practices with pandas dataschool.io

Published June 3, 2019 under Data Science

The pandas library is a powerful tool for multiple phases of the data science workflow, including data cleaning, visualization, and exploratory data analysis. However, the size and complexity of the pandas library makes it challenging to discover the best way to accomplish any given task.

Pandas

An Introduction to Convolutional Neural Networks victorzhou.com

Published May 23, 2019 under Neural Networks

There’s been a lot of buzz about Convolution Neural Networks (CNNs) in the past few years, especially because of how they’ve revolutionized the field of Computer Vision. In this post, we’ll build on a basic background knowledge of neural networks and explore what CNNs are, understand how they work, and build a real one from scratch (using only numpy) in Python.

AI, Convolutional NN, Python

Exploring Stock Price Movements After Major Events medium.com

Published May 19, 2019 under Trading

This post will go through the process of gathering and cleaning this data followed by an exploratory analysis examining price trends and the impact of events on prices using data from the IEX API and scraped events from financial news sites.

API, IEX, Python, Quant Trading

Understanding PyTorch with an example: a step-by-step tutorial towardsdatascience.com

Published May 15, 2019 under Machine Learning

PyTorch is the fastest growing Deep Learning framework and it is also used by Fast.ai in its MOOC, Deep Learning for Coders and its library. PyTorch is also very pythonic, meaning, it feels more natural to use it if you already are a Python developer.

Python, PyTorch

Python for NLP: Introduction to the Pattern Library stackabuse.com

Published May 3, 2019 under Data Science

The Pattern library is a multipurpose library capable of handling the following tasks:

  • Natural Language Processing: Performing tasks such as tokenization, stemming, POS tagging, sentiment analysis, etc.
  • Data Mining: It contains APIs to mine data from sites like Twitter, Facebook, Wikipedia, etc.
  • Machine Learning: Contains machine learning models such as SVM, KNN, and perceptron, which can be used for classification, regression, and clustering tasks.

In this article, we will see the first two applications of the Pattern library from the above list. We will explore the use of the Pattern Library for NLP by performing tasks such as tokenization, stemming and sentiment analysis. We will also see how the Pattern library can be used for web mining.

Machine Learning, NLP

Python at Netflix medium.com

Published May 3, 2019 under Python

As many of us prepare to go to PyCon, we wanted to share a sampling of how Python is used at Netflix. We use Python through the full content lifecycle, from deciding which content to fund all the way to operating the CDN that serves the final video to 148 million members. We use and contribute to many open-source Python packages, some of which are mentioned below. If any of this interests you, check out the jobs site or find us at PyCon. We have donated a few Netflix Originals posters to the PyLadies Auction and look forward to seeing you all there.

Programming

End-To-End Topic Modeling in Python towardsdatascience.com

Published April 26, 2019 under Data Science

Topic Model: In a nutshell, it is a type of statistical model used for tagging abstract “topics” that occur in a collection of documents that best represents the information in them.

Many techniques are used to obtain topic models. This post aims to demonstrate the implementation of LDA: a widely used topic modeling technique.

Machine Learning, NLP, Topic Modelling

When to ‘Buy the Dip’ towardsdatascience.com

Published April 26, 2019 under Trading

“Buy the dip” — it’s a frustratingly simple piece of advice. Like most pieces of advice, it’s easier said than done and the giver of such advice has probably not attempted to practice what they preach. It induces FOMO, which leads to the “hope trade”, when the “hope trade” goes awry you’re stuck as the “long term investor” who “really believes in the company’s mission”.

Markov Models, Python, Volatility

Building a Robinhood Stock Trading Bot medium.com

Published April 26, 2019 under Trading

The bot is written in Python and relies on two core libraries for the majority of its functionality: robin-stocks and ta. robin-stocks is a library that interacts with the Robinhood API and allows one to execute buy and sell orders, get real time ticker information, and more. ta is a technical analysis library that also incorporates the Python Pandas library to generate indicators from stock data.

Algorithmic Trading, Pandas, Technical Analysis

OpenCV-Python Cheat Sheet: From Importing Images to Face Detection fritz.ai

Published April 21, 2019 under Machine Learning

Cropping, Resizing, Rotating, Thresholding, Blurring, Drawing & Writing on an image, Face Detection & Contouring to detect objects. All Explained.

Computer Vision, OpenCV, Python

Pandas DataFrames for Data Analysis kite.com

Published April 19, 2019 under Data Science

In this post, we’ll learn about Pandas, a high-performance open-source package for doing data analysis in Python.

We’ll cover:

  • What Pandas is and why should you use it.
  • What a Pandas DataFrame is.
  • Creating and viewing a DataFrame.
  • Manipulating data in a DataFrame.

DataFrame, Pandas

How to Automate Tasks on GitHub With Machine Learning for Fun and Profit towardsdatascience.com

Published April 19, 2019 under Machine Learning

A tutorial on how to build a GitHub App that predicts and applies issue labels using Tensorflow and public datasets.

Python, TensorFlow

Awesome Data Science with Python github.com

Published April 19, 2019 under Python

A curated list of awesome resources for practicing data science using Python, including not only libraries, but also links to tutorials, code snippets, blog posts and talks. So. Much. Python.

Awesome, Data Science

PyTorch Sentiment Analysis github.com

Published April 19, 2019 under Machine Learning

Tutorials covering how to do sentiment analysis using PyTorch 1.0 and TorchText 0.3 using Python 3.7.

The first 2 tutorials will cover getting started with the de facto approach to sentiment analysis: recurrent neural networks (RNNs). The third notebook covers the FastText model and the final covers a convolutional neural network (CNN) model.

Data Science, Python, PyTorch

Text Classification in Python Using spaCy dataquest.io

Published April 19, 2019 under Machine Learning

NLP, spaCy, Text Classification

Vaex: A DataFrame with super strings towardsdatascience.com

Published April 19, 2019 under Data Science

String manipulations are an essential part of Data Science. The latest release of Vaex adds incredibly fast and memory efficient support for all common string manipulations. Compared to Pandas, the most popular DataFrame library in the Python ecosystem, string operations are up to ~30–100x faster on your quadcore laptop, and up to a 1000 times faster on a 32 core machine.

Pandas, Vaex

Introduction to Anomaly Detection in Python floydhub.com

Published April 19, 2019 under Python

The very basic idea of anomalies is really centered around two values – extremely high values and extremely low values. Then why are they given importance? In this article, we will try to investigate questions like this. We will see how they are created/generated, why they are important to consider while developing machine learning models, how they can be detected.

Anomaly Detection, Time Series Analysis

Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib

Published April 18, 2019 under Books

Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more.

Matplotlib, Numpy, SciPy

Learning Python, 5th Edition

Published April 18, 2019 under Books

Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz’s popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python. It’s an ideal way to begin, whether you’re new to programming or a professional developer versed in other languages.

Python

Python for NLP: Introduction to the TextBlob Library

Published April 18, 2019 under Data Science

In this article, we will explore TextBlob, which is another extremely powerful NLP library for Python. TextBlob is built upon NLTK and provides an easy to use interface to the NLTK library. We will see how TextBlob can be used to perform a variety of NLP tasks ranging from parts-of-speech tagging to sentiment analysis, and language translation to text classification.

NLP, TextBlob

Pyodide: Bringing the scientific Python stack to the browser mozilla.org

Published April 18, 2019 under Python

Pyodide is an experimental project from Mozilla to create a full Python data science stack that runs entirely in the browser.

Programming

Kalman and Bayesian Filters in Python github.com

Published April 17, 2019 under Data Science

Bayesian Analysis, Kalman Filter

Random Forests for Complete Beginners victorzhou.com

Published April 15, 2019 under Data Science

Machine Learning, Random Forest

Comprehensive Python Cheatsheet github.io

Published April 9, 2019 under Python

Programming

How to Version Control Jupyter Notebooks nextjournal.com

Published April 9, 2019 under Python

Jupyter

Improve your trading performance with Trade Blotter tradeblotterapp.com

Published March 30, 2019 under Trading

Quant Finance

Data Science with Python explained towardsdatascience.com

Published March 30, 2019 under Data Science

Numpy, Python, scikit-learn, XGBoost

Build your first Convolutional Neural Network to recognize images toptal.com

Published March 30, 2019 under Neural Networks

Computer Vision, Convolutional NN, Data Science

Building a Raspberry Pi security camera with OpenCV pyimagesearch.com

Published March 30, 2019 under Computer Vision

OpenCV, Raspberry Pi

Machine Learning for Beginners: An Introduction to Neural Networks victorzhou.com

Published March 9, 2019 under Neural Networks

AI, Deep Learning, Machine Learning

Breast cancer classification with Keras and Deep Learning pyimagesearch.com

Published March 3, 2019 under Neural Networks

Computer Vision, HealthTech, Keras

A Complete Machine Learning Project Walk-Through in Python codequs.com

Published March 3, 2019 under Machine Learning

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