This is the current state of my coding bookshelf. On the top shelf there are financial coding, Python & R machine learning, Coding Interview, Algorithems, financial modeling, and R programming books. Bottom shelf has references for statistics, Coding, and Financial Risk Management. I like to study from several different books on the same topic. I find that different authors have varying approaches, and they work best in combination. Jake VanderPlas's Python Data Science Handbook (c 2017) is still my best book for learning Python data science libraries. It's my goto book for numpy, matplotlib, sklearn and Jupyter Notebook's %magic and !shell commands.
Scientists estimate the time to a working commercial quantum computer at 10 years to maybe never. Error correction needs of qubits pose unknown challenges. A free downloadable study on the state of quantum computing is available from The National Academies Press.
Easy Explanation on How A Quantum Computer Works
Date: March 20, 2019
This is an old video dated 2013, but has an easy to understand explanation on how a quantum computer works. 2^n is the number of information bits that can theoretically be combined. 2^300 is supposed to be a greater number than the number of [atoms] in the universe. But this is only useful for calculations that can make use of the super-position state. Also, for reading the final result, the quantum computer must drop back out of the super-position state into the normal state. For normal calculations, the quantum computer is projected to be slower than a regular computer.
Beginning of Probability Measure Theory
Date: March 20, 2019
I think one of the most confusing and difficult part of learning probability measure theory comes at the very beginning! Obviously this project is going to be very opinionated. :-) DeMorgan's Laws and other rules for calculating probabilities, which comes after the beginning, are not that different from normal algebra. I think most people can follow along and understand the other parts, if they do not make the mistake of getting forever stuck on the starting definitions! We need to rename "probability space", "sigma-algebra", and all those greek letters, to something more English-like and easier to remember. Anyway, I plan to post a very opinionated translation from Greek-Math-speak to Normal-English-speak.
Next Meetup: Convolutional Neural Networks for Visual Recognition, by Stanford University, Chp 1 and 2. CS231n
Reference: Gareth James et al., An Intro to Stat Learn with R. ISLR-website
I gave my first short talk on a data science subject to a local Meetup group this week. Here's a shout-out to the group,
Serious Data Science. Thanks Deborah, Julius, Elsa, Peter, and others. You guys are so supportive and kind! I don't think I would have read the ISLR book with such attention without all of you helping to keep my motivation high! :-) If you, Reader, live near Sterling, Virginia, please come and join this wonderful Meetup group. We meet monthly on the 2nd Tuesday evenings at REI Systems Inc building.
GARP 20th Conference in NYC
Date: February 24 - 27, 2019
I will be in NYC attending the 20th GARP Risk Conference. The agenda has several sessions on machine learning and AI along with the usual risk topics. I am interested in learning more about how data science and AI is being using by financial institutions. I will also catch up with my former colleagues from the SEC while I am there. Glad the scheduling worked out.
PyData DC 2018 and SciPy Austin 2018
Date: November 20, 2018
Attended the PyData DC 2018 conference in Tysons Corner, VA over the weekend. I thoroughly enjoyed it. Everybody was very nice and welcoming towards relatively new programmers, like myself. I will post a write-up about several talks/software that caught my attention. This conference was more accessible for me than SciPy in July 2018 at Austin, TX. I come from a business background and have been learning Python and Data Science for only about 1.5 years. Many of the people I talked to at PyData had similar backgrounds. The SciPy community was more deeply into core python package development and were more advanced programmers. The majority seemed to have PhDs in a hard science or math field. For me personally, the learning experience was higher from the SciPy conference in a "tough love" way. But I felt more of a sense of belonging and was happier at the PyData conference. I will also have a writeup of a couple of the tools/talks I found most useful from the SciPy 2018 conference.
DevEnv for Windows - Elegant-SciPy book:
I agreed to help Juan write a Windows OS version of "build" instructions for converting Markdown format files on GitHub to Jupyter notebooks, which are then saved as html or pdf book chapters, with or without output calculation cells. I tested several versions so far using conda virtual environment, partial bash tools for Windows, and the new Microsoft Windows Subsystem for Linux (for fully compatible bash scripts).
Note on Jupyter notebooks: MikTex package needs to be installed at "C:/Program Files" and the Windows environment variable, system path needs to be set to this directory. MikTex allows LaTex and some markdown formatting codes to work for saving Jupyter notebooks to html and pdf formats.
Proposed talk to Risk Managers:
Part 1) A quick overview of cool talks from SciPy and PyData conferences.
Part 2) A hands-on practical demos on the most useful AWS tools.
How to host your website on Amazon Route 53
How to run a Python program on Amazon Lambda
How to run a deep learning project on Amazon EC2
(Elastic Compute Cloud)
How to store your files on Amazon S3 (Simple Storage Service)
Bonus - how to share your project on GitHub, and how to find other people's projects.
Refactoring previous code to share on my portfolio
Random Walk charting demo
A sorting algorithm and analysis using Big-O
Game 2048 slider, using my custom images and class objects.
Python IO demo. Writing text files for controlling lab equipment settings.
Uploading previous R code for matrix calculation and data analysis