This is a great introduction to Deep Learning. I know I learned a few things from Phillip. Some key concepts RapidMiner now can do GPU deep learning. Supports NVIDIA. Easy: Using already loaded Neural Net operators. Harder: Using H20.ai Deep Learning operator. Hardest: Using Keras with RapidMiner. Keras requires more complex setup with RapidMiner. CNN, RNN, LSTM, etc are now available via RapidMiner GUI. Keras supports Tensorflow, CNTK, and Theano. Need Python v3.5 (see installation guide here). ‘Deep’ discussion on Recurrent and Covolutional. Misc Here’s a nice graphic from KD Nuggets on some basic activation functions. These activiation functions are what switch a neuron on or off in a neural net model. ...

I’ve been keeping a lazy eye on Orange over the years and it’s (fairly) recent update has made it quite an impressive contender in the Data Science visual platform space. While it’s not RapidMiner, it does have a lot of great things going for it. First, it’s entire core was rewritten to tightly integrate with Scikit-Learn and Python. It has a decent time series ‘add-on’ which comes stock with ARIMA. It has a really good Text Processing ‘add-on’ that gives the user more finer control that RapidMiner’s and it has a great GEO Map natively. ...

File this under “no shit Sherlock,” but hackers are already weaponizing machine learning. The AI, named SNAP_R, sent simulated spear-phishing tweets to over 800 users at a rate of 6.75 tweets per minute, luring 275 victims. By contrast, Forbes staff writer Thomas Fox-Brewster, who participated in the experiment, was only able to pump out 1.075 tweets a minute, making just 129 attempts and luring in just 49 users. via Gizmodo In reality the above example is just a bunch of loops and stuff, it’s what the tweets contain and to whom it’s sent to that makes all the difference. That ‘intelligence’ is probably generated from some machine learned model or “AI”. ...

This is Medium toll-gated but worth a good read. PyTorch rules them all these days and rightfully so. Whether eager execution makes your program is a little slower or a lot slower depends on how much of your computation is spent in high arithmetic intensity ops like conv or matmul. IE, doing pure matrix multiplications (longer than 1 millisecond) is not much different whether you use TensorFlow eager, PyTorch or TensorFlow classic. ...

One of the big corporate strategy things I worked on was developing and putting into production a PQL model. It was essentially a propensity to buy model that analyzed usage patterns on the RapidMiner software platform and bucketed new downloaders into those that were likely to buy or not buy. It was incredibly successful and helped the sales team focus on thier leads better. My former colleague Tom shares his thoughts on it since it’s been in production for over a year now. Here are my interesting tid bits. ...

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. That’s the way to success, the rest is just noise and a waste of time. This lesson can be applied to everything in life. Data is your friend We generate data all the time, whether it’s captured in a database or spreadsheet, just by being alive you throw of data points. The trick is to take notice of it, capture it, and then do something with it. It’s the “do something with it” that matters to your success or not. Your success can be anything that is of value to you. Time, money, weight loss, stock trading, whatever. You just need to start capturing data, evaluate it, and take action on it. ...

A few months ago I read about a programmer that automated his job down to the point where the coffee machine would make him lattes! Despite the ethical quandary, I thought it was pretty cool to automate your job with scripts. Then I wondered, was it possible to automate data science? Or at least parts of it? This general question proved to be a rabbit hole of exploration. StackExchange has an ongoing discussion into another programmer’s automation of his tasks. He used scripts to prepare customer data into spreadsheets that other employees would use. The task used to take a month to do but it was able to cut that time down to 10 minutes. It did take him several months figure out how to build the right scripts to do the work he now only works 1 to 2 hours a week and gets paid for 40 hours. ...

This is just nuts. Millennials just can’t seem to catch a break. Now AI is coming for their jobs. Research released by Gallup on Thursday indicates a collision between technology and “business as usual” is coming soon, and the fallout will be ugly, especially for Millennials. Automation and artificial intelligence (AI) are among the most disruptive forces descending upon the workplace, says the Gallup report, and 37% of Millennials “are at high risk of having their job replaced by automation, compared with 32% of those in the two older generations.”[via Forbes] ...

I just capped off a 4 day RapidMiner training class in NYC last week. It was a ton of fun and I got to meet lots of super smart and cool people. While I didn’t get to take photos of the class (I was giving training), Professor @BariAnasse got a few of me. Thanks Prof! @neuralmarket Great data science training by the expert Thomas Ott of RapidMiner So much to learn from Thomas @ingomierswa @RapidMiner #NYC pic.twitter.com/HehS2hSdbr ...

Boy oh boy the real estate market is back with a vengeance. All of my Architect and Engineering friends are busier than right before the 2007 meltdown. Building additions, knockdowns, and the like are flooding in. It seems that everyone just woke up this year and got busy. Flippers are back And the Flippers are back… My real estate investing friends (aka Flippers) are snapping up house at County Court Houses for pennies on the dollar, cleaning and fixing them, then flipping back onto the market. My Realtor friends are busy as well too, new showings and new sales. Everyone seems to be happy and whispers of “the good times” are back again. ...