I share some notes from an online community meetup on doing Time Series in H2O-3 with the Modeltime R package. The new R Package is neat, I hope that someone builds something like that for Python!
Facebook sent a cease and desist letter to NYU researchers looking into their targed political ads.
A comprehensive list of my Python Tutorials for SEO, plain work, and other fun stuff.
How to install Tensorflow and Julia on a Jetson Nano. My tips to prevent baldness (aka ripping your hair out!)
New Open Source Libraries hint at AI programming blocks evolving as it learns.
Generating Forex Charts with the Open Source Bokeh charting library.
There’s a lot of AI Snakeoil out there, get it from reputable sources!
Google’s AlphaGo makes a Go Master Quit. It’s too powerful.
As far as the trend in AI goes, it’s really getting started now. AI is being adopted by the next tier of companies.
The market is an average and there are funds or AI traders that beat the average, but others will underperform the average
I’ve finally found a way to download SEC.GOV data in a consistent and less stressful way.
On the heels of my last post, I’ve extended those functions to the EURUSD pair. The data starts from this year 2019 and goes through to yesterday. It’s actually a pretty neat script as it takes data from Onada and then generates the support and resistance lines for that particular pair. The next step would be to create a buy/sell order in the Oanda Practice Account. Once I do that it’s then a matter of writing a trading strategy and testing it in real time.
H2O.ai releases Isolation Forests for open source, an anomaly detection method.
A short introduction on Machine Learning Interpretability (MLI). Learn the basics of MLI.
Learn how Machine Learning is making your food more delicious. Pesto dal vivo!
A reat presentation on the upcoming release of TensorFlow v2 by Martin Wicke.
I share my notes from this must watch if you’re thinking of putting AI models into production.
What’s the different between Functional Programming and OOP for Python? I share my notes from this great talk.
Learn about the complex feature engineering that Driverless AI does. How to squeeze more performance out of your machine learning models.
New updates to Driverless AI keep pushing the innovation envelope. Learn about key differentiators.
Julia announced Flux, a machine learning frame work for Julia.
Overfitting and introducing bias during model training is always a big topic in data science. Typically you train a model using Cross Validation by creating a model on k-1 folds and test it on the remaining one fold. This one fold is the holdout set and will usually work very well if, and only if, the trained model is independent of the holdout set. Under normal situations, this works well, but you might begin to leak information into the model as the test fold changes.
I’ve been think a lot about open source lately. I’ve also been thinking of closed source and open core too.
In this video the presenter goes over a new R package called ‘iML.’ This package has a lot of power when explaining global and local feature importance. These explanations are critical, especially in the health field and if your under GDPR regulations. Now, with the combination of Shapley, LIME, and partial dependence plots, you can figure out how the model works and why. I think we’ll see a lot of innovation in the ‘model interpretation’ space going forward.
I stumbled across an interested reddit post about using matrix factorization (MF) for imputing missing values. The original poster was trying to solve a complex time series that had missing values. The solution was to use matrix factorization to impute those missing values. Since I never heard of that application before, I got curious and searched the web for information. I came across this post using matrix factorization and Python to impute missing values.
Why most Twitter Bots suck terribly at what they are supposed to do. The entire social media gamification stinks to high hevean.
Understanding the LIME framework for Machine Learning Interpretability.
A few years ago RapidMiner incorporated a fantastic open source library from H2O.ai. That gave the platform Deep Learning, GLM, and a GBT algos, something they were lacking for a long time. If you were to look at my usage statistics, I’d bet you’d see that the Deep Learning and GLM algos are my favorites. Just late last year H20.ai released their driverless.ai platform, an automated modeling platform that can scale easily to GPUs.
Continuing the stream of consciousness from my Working with Instgram API, JSONPath, and RapidMiner post, I started beta testing a new and improved Instagram Hashtag Tool. I’ve even opened it up to a few beta testers (ping me if you want to try it). It uses a RapidMiner Server on the backend to watch a Dropbox folder. Once you put a text file into the ‘In’ folder, it triggers a process and spits back a spreadsheet in the ‘Out’ folder.
My experiences in learning Data Science and working at a Startup. I share my lessons learned and tips on how you can get started in the field of Data Science.
Coming on the heels of “I told you so,” China is using facial recognition to make sure you’re a good Chinese citizen. Authorities in Shenzhen, China, have set up artificial intelligence-powered CCTV cameras to scan the faces of those who jaywalk at major intersections and display their identities on large LED screens for all to see. If that isn’t punishment enough, plans are now in place to link the current system with cellular technology, so offenders will also be sent a text message with a fine as soon as they are caught crossing the road against traffic lights.
Learn about data science and machine learning from a Google presentation
Learn how one group of Hackers developed a machine learning framework from scratch!s
Learn how StockTwits uses Data Science and Machine Learning to build new products. Read my notes from Garrett Hoffman’s interview.
The news dropped that Google’s new implementation of AlphaGo, called AlphaGO Zero, was able to learn completely on its own. No training set was first used, rather it built it’s own training set as it played against the older AlphaGO. Earlier versions of AlphaGo were taught to play the game using two methods. In the first, called supervised learning, researchers fed the program 100,000 top amateur Go games and taught it to imitate what it saw.
A great introduction to Deep Learning, Keras, and installing it on RapidMiner. A video and my notes.
Is Orange3 an alternative to RapidMiner? For one, it’s tightly woven with Scikit-Learn and open source. This might a great intro to a code + code free interface!
AI is being weaponized at a scale never before seen. Fake deep learning videos are going to be able to skew electons
What is a PQL model and how does it work? A novel approach to freemium types of product launches.
An important lesson I’ve learned while working at a Startup is to do more of what works and jettison what doesn’t work, quickly
Evaluating whether Data Science can be fully automated. The answer? Somethings can be but not everything.
Millennials have been given a raw deal from the Boomers. Gen X’ers know all about that.
How to use process mining with RapidMiner to make your processes go smoother and faster.
I have a daily downtime routine. Every evening I set aside about a hour and think. I sit or walk around the house and ruminate about all sorts of random things. Sometimes it’s with a glass of wine and more often it’s with a cup of black tea and milk. Sometimes my mind wanders to what I did that day or what I didn’t finish. Other times I get inspired to write a new blog post or create a new tutorial.
The Pi has been a great thin client and a small, but capable server. I’ve used it for my Personal Weather Station project and as an FTP server.
Assigning China’s Harmony Score is the first step to complete control of 1 Billion People.
Where my Machine Learning articles have been published. A running list when I remember to update it.
The world right now is awash in Predictive Analytics, the mystery of Big Data, and the rise of the glorious and magical Data Scientist. Most of the time we hear these buzz words in relation to some marketing campaign, election, or credit score application process, but what about applying these tools and people to a project that can benefit the welfare of humanity? Well, one group of data scientists and a healthcare provider in Washington State are doing just that.
My thoughts on Big Data and how throwing everything into it is a bad idea.
Makes business sense, right? Keep the best customers happy, group the customers that are about to leave into the ones you can save or not.
Nothing but blue skies I see, and the grass on the side looks pretty green!
I finally got my Personal Weather Station (PWS) to upload current weather data to Wunderground1 last night. You have no idea how happy this made me, considering I started this project over a year ago but then got interrupted with “life.” I dedicated my first Raspberry Pi to Bitcoin mining, so I needed a second one (Pi’s are addicting, and cheap) to finally get my PWS up and running on the Internet.
Today’s guest post about an awesome new plugin for Rapidminer, is from Milan Vukicevic. Although I walked in at the very end of his presentation at RCOMM 2010, I sat down with Milan on my last day and he gave me a personal demo of WhiBo. The applications I see from this plugin, as it relates to the financial world, is its ability to build algorithms on new data, find patterns, and tweak parameters that were never possible before.
Using Support Vector Machines (SWM) to predict residential electrical usage.
the OECD Factbook Explorer is a really great free tool to do research on countries outside the USA.
AlphaGo and Lee Sedol showed us what it means to be human in the Game of Go.
In my old blog, Digital Breakfast, I posted a few times about my desire to build an Automated Trading System (ATS) using Excel
I heard this first on Bloomberg Radio and then found the article. It’s about the ever increasing use of data mining and AI in the financial markets. In his cubicle overlooking the trading floor, Kearns, 44, consults with Lehman Brothers traders as Ph.D.s tap away at secret software. The programs they’re writing are designed to sift through billions of trades and spot subtle patterns in world markets. Kearns, a computer scientist who has a doctorate from Harvard University, says the code is part of a dream he’s been chasing for more than two decades: to imbue computers with artificial intelligence, or AI.