Effectively Using Matplotlib

The python visualization world can be a frustrating place for a new user. There are many different options and choosing the right one is a challenge. For example, even after 2 years, this article is one of the top posts that lead people to this site. In that article, I threw some shade at matplotlib and dismissed it during the analysis. However, after using tools such as pandas, scikit-learn, seaborn and the rest of the data science stack in python – I think I was a little premature in dismissing matplotlib. To be honest, I did not quite understand it and how to use it effectively in my workflow.

Now that I have taken the time to learn some of these tools and how to use them with matplotlib, I have started to see matplotlib as an indispensable tool. This post will show how I use matplotlib and provide some recommendations for users getting started or users who have not taken the time to learn matplotlib. I do firmly believe matplotlib is an essential part of the python data science stack and hope this article will help people understand how to use it for their own visualizations.

http://pbpython.com/effective-matplotlib.html

A Guide for Applying Machine Learning Techniques in Finance

Does it sound familiar to you? In order to get an idea of how to choose a parameter for a given classifier, you have to cross reference to a number of papers or books, which often turn out to present competing arguments for or against a certain parameterization choice but with few applications to real-world problems.

For example, you may find a few papers discussing optimal selection of K in K-nearest Neighbour, one supporting so-called square-root of sample size N method, another talking about selecting K based on how well the classifier performs according to its cross-validation samples. The parameterization choices have signficant impacts on the performances of classifiers; so it’s important to get them right. Parameterized differently, as shown in the paper below, the performances of each of the 8 most popular classification algorithms can be significantly different.

The following 51-page paper introduces 8 most popular classifiers in Machine Learning and illustrates each with an example based on financial data from real world. It can serve as a guide for how to apply Machine Learning Techniques to solve problems faced by finance industry: https://ssrn.com/abstract=2967184.

Please see the presentation slides that present a summary of classification techniques used in finance industry: https://ssrn.com/abstract=2973065.

http://www.datasciencecentral.com/profiles/blogs/a-guide-for-applying-machine-learning-techniques-in-finance

Practical Promises in JavaScript – Finally

For Those Just Tuning In…

If you are just joining us, here is what you have missed so far:

In part 1, we talked about what promises are and what they can be used for.

In part 2, we started looking at how we can create promises.

Then in part 3, we saw how each call to then actually makes a new promise, and that those promises can be chained together.

In part 4, we learned how to combine promise chaining with the creation of new promises in order to simplify complex async workflows.

In part 5, we applied everything we have learned so far to create a nice, clean API that unwraps a complex result object via promise chaining.

In part 6, we explored what happens to our promises when we call then and catch in different orders.

Are we finally done yet?…

http://trycatchfail.com/blog/post/Practical-Promises-in-JavaScript-Finally

Over 150 of the Best Machine Learning, NLP, and Python Tutorials I’ve Found

While machine learning has a rich history dating back to 1959, the field is evolving at an unprecedented rate. In a recent article, I discussed why the broader artificial intelligence field is booming and likely will for some time to come. Those interested in learning ML may find it daunting to get started.

As I prepare to start my Ph.D. program in the Fall, I’ve been scouring the web for good resources on all aspects of machine learning and NLP. Typically, I’ll find an interesting tutorial or video, and that leads to three or four more tutorials or videos, and before I know it, I have 20 tabs of new material I need to go through. (On a side note, Tab Bundler has been helpful to stay organized.)

After finding over 25 ML-related “cheat sheets”, I created a post that links to all the good ones.

To help others that are going through a similar discovery process, I’ve put together a list of the best tutorial content that I’ve found so far. It’s by no means an exhaustive list of every ML-related tutorial on the web — that would be overwhelming and duplicative. Plus, there is a bunch of mediocre content out there. My goal was to link to the best tutorials I found on the important subtopics within machine learning and NLP.

By tutorial, I’m referring to introductory content that is intending to teach a concept succinctly. I’ve avoided including chapters of books, which have a greater breadth of coverage, and research papers, which generally don’t do a good job in teaching concepts. Why not just buy a book? Tutorials are helpful when you’re trying to learn a specific niche topic or want to get different perspectives.

I’ve split this post into four sections: Machine LearningNLPPython, and Math. I’ve included a sampling of topics within each section, but given the vastness of the material, I can’t possibly include every possible topic.

For future posts, I may create a similar list of books, online videos, and code repos as I’m compiling a growing collection of those resources too.

If there are good tutorials you are aware of that I’m missing, please let me know! I’m trying to limit each topic to five or six tutorials since much beyond that would be repetitive. Each link should have different material from the other links or present information in a different way (e.g. code versus slides versus long-form) or from a different perspective.

https://unsupervisedmethods.com/over-150-of-the-best-machine-learning-nlp-and-python-tutorials-ive-found-ffce2939bd78

Synchronizing Amazon S3 Buckets Using AWS Step Functions

In my free time, I run a small blog that uses Amazon S3 to host static content and Amazon CloudFront to distribute it world-wide. I use a home-grown, static website generator to create and upload my blog content onto S3.

My blog uses two S3 buckets: one for staging and testing, and one for production. As a website owner, I want to update the production bucket with all changes from the staging bucket in a reliable and efficient way, without having to create and populate a new bucket from scratch. Therefore, to synchronize files between these two buckets, I use AWS Lambda and AWS Step Functions.

In this post, I show how you can use Step Functions to build a scalable synchronization engine for S3 buckets and learn some common patterns for designing Step Functions state machines while you do so.

https://aws.amazon.com/pt/blogs/compute/synchronizing-amazon-s3-buckets-using-aws-step-functions

1kb JavaScript library for building frontend applications

HyperApp is a JavaScript library for building frontend applications.

  • Declarative: HyperApp’s design is based on the Elm Architecture. Create scalable browser-based applications using a functional paradigm. The twist is you don’t have to learn a new language.
  • Custom tags: Build complex user interfaces from custom tags. Custom tags are stateless, framework agnostic and easy to debug.
  • Batteries-included: Out of the box, HyperApp has Elm-like state management and a virtual DOM engine; it still weighs 1kb and has no dependencies.

https://github.com/hyperapp/hyperapp