You're correct - it doesn't get into the deep learning aspect yet.
This article is in fact the second part of a larger planned series. The idea is to present more depth than a "quick refresher" that seems to be common to a lot of blog posts, but far less material than would be found in a 10-week (or single semester) undergraduate course.
The Strang book: "Introduction to Linear Algebra" does seem to have quite a few "high quality" 1-star reviews. They mostly seem to cluster around this book not being good for a linear algebra introduction, for someone new to the field. Typical comments:
"It seems the reviewers who think this book is wonderful for non-math majors are math majors!"
...I of course expect that any popular book will have 1 star reviews, especially by people who had a hard time with the material. Anyone have thoughts whether the Strang book is better as a supplement to other texts in coming up to speed on Linear Algebra? Other book recommendations?
Disclaimer: I run a site discussing Python/ML topics as applied to quant finance.
Python is primarily used because the machine learning libraries within it are very mature and play nicely with each other.
It is easy to get started in Python (and most of its libraries) by downloading the freely-available Anaconda distribution. This usually "just works", cross-platform. The language itself is extremely straightforward to pick up.
Within the Python ecosystem there are many mature libraries. In particular NumPy was written for carrying out vectorised computation. This enabled more libraries, such as pandas (for dataframe manipulation), SciPy (for general scientific computation) and scikit-learn (for ML) to be developed. Each of these libraries also possess clean and consistent APIs for carrying out their specialty tasks.
Thus it becomes straightforward in Python to import data from many sources, "wrangle" it into the correct format (even with real-world, messy data), put it into an ML data pipeline and then visualise it easily (via Matplotlib or Seaborn). In addition there is Jupyter for straightforward "notebook" style research.
Finally, Theano and TensorFlow are two great deep learning libraries. There are a few hiccups on installation sometimes, but for the most part they "just work".
There are still some "missing pieces" however. The statsmodels library does a good job of time series analysis, but it doesn't yet compete fully with R in this respect.
Julia is also likely to make serious inroads into Python's usage in the near future. I'm excited about where the project is heading.
I'm actually in the process of overhauling the design of the site, particularly with regards to mobile, as the current Bootstrap-derived design pushes all sidebar content to the top on mobile/table.
Indeed, this is the latest article and it does link back to parts 1 & 2.
Each part of the series is designed to cover the "typical" modules on a UK four-year undergraduate Masters of Mathematics degree.
However, it can be challenging in the latter two years to include a broad enough set of modules to cover all interests, so I have had to stick to those "core" modules likely to be found in many degrees, as well as those more specifically related to quant finance and machine learning.
However, it is my hope that individuals will be motivated to look at other areas as well, even if they're not directly related to career paths!
Disclaimer(s): QuantStart.com founder here, background as a quant dev at a small fund.
I should probably nuance my statement that it is easy to find trading strategies by saying that it is easy to find new trading /ideas/. There are a huge number of freely available trading ideas on forums, pre-print servers (arXiv, SSRN), blogs etc. The trick is knowing how to implement them properly, accounting for any transaction costs and adjusting the parameters of the model. This is often where the stated performance falls down. It takes a lot of time to carry out this sort of research.
Long-term profitable strategies are tricky to find, due to the ever-present spectre of "alpha decay". This is where your strategy's edge is "arb'd out" - everyone else knows what you're doing and so there's no tradeable edge anymore. Hence it is necessary to have a portfolio of strategies and gradually phase out the ones that aren't doing well, and bring in new ones over time.
That being said there are a large number of trend following funds (known as Commodity Trading Advisors, or CTAs, in the industry) that all broadly do the same thing (follow "trends" in the commodity futures markets) and have great years every now and then. There are some well-known "retail" quant traders who do well by trend following, but it does require quite a bit of capital to trade in futures.
The philosophy that I do try to emphasise is to always be learning and researching new ideas. Also, as you mention, I'm pretty keen on discussing the math(s)/statistics aspect because once you have a solid math capability, it is easier to see where potential edges might exist and how to really assess whether it is a true "edge" or just a statistical anomaly.
I believe someone else in a grandchild comment below said that there are many areas that bigger quant funds won't touch because of institutional incentives. If you have $10bn assets under management (AUM), then you're not going to care about investing $100-200k, even if the returns are good, because it won't move the needle on your monthly reports.
The trick is to niche down into markets that you can spend a lot of time researching to find a distinct edge, that won't likely be touched by bigger funds. One area that is becoming interesting recently, due to the prevalence of satellite data/AI/deep learning-esque VC-backed startups, is building commodity supply/demand models. A good example is forecasting oil supply/demand by analysing large quantities of storage tank heights in global refineries [1].
Also, a small related-to-Zipline plug: I've recently started a free Python-based MIT-licensed open-source backtester [2], predominantly as a learning tool for programming and quant trading. There's about 4-5 of us working on it at the moment and it's in an early alpha stage, but we're always looking for people willing to help.
When researching for the article I was actually rather surprised that I couldn't find many MOOCs on aeronautical, civil, electrical, chemical or mechanical engineering.
While it's pretty straightforward to find open courses/content on Linear Algebra and Calculus, there's very little on, say, Compressible Flow/Gas Dynamics or Turbomachinery, for instance.
If anybody knows of any courses on topics related to the above engineering disciplines, I'd love to take a look.
I also agree that at the end of part 2, one would have sufficient "mathematical maturity" to handle most commercial environments.
Also, John Kruschke's "Doing Bayesian Data Analysis", aka the "Puppy Book", is a gentle introduction to Bayesian inference. It is very readable, especially with regards MCMC.
This article is in fact the second part of a larger planned series. The idea is to present more depth than a "quick refresher" that seems to be common to a lot of blog posts, but far less material than would be found in a 10-week (or single semester) undergraduate course.
Thanks for the Stanford link. I'll check it out.