Elon Musk has just bought Twitter: tell him there are 101 people ready to make the platform more and more a concrete, transparent and ethical business model! Seriously, an award to take the breath away: thanks to Engati and congrats to all friends and colleagues who share the values of the community with me, a digital communication center where companies can find professionals with a global vision to rely on with tranquility to be represented (the algorithms for the expert analysis methodology do not lie), while the citizens a place to have verified information. "101 Global Twitter Influencers to follow 2022" https://lnkd.in/gyP6hiMA

Building an AI that could understand and speak back to me has always been a dream of mine. Ever since I saw Iron Man's Jarvis in action, the urge to actually try it became more intense. My dream has now become true. In this story I'll go through how to build an AI that can understand and speak back to you using astibob and golang. It will be able to repeat what you're saying. For teaching purposes we'll split abilities in different workers, but bare in mind that all abilities can be on the same worker if it makes more sense.

On a regular basis people tell me about their impressive achievements using AI. This post may come off as a rant, but that's not so much its intent, as it is to point out why we went from having very few AI experts, to having so many in so little time. Also to convey that most of these experts only seem experty because so few people know how to call them on their bull shit. So you converted 11 lines of python that would fit on a t-shirt to Java or C or C . You have mastered what a cross compiler can do in 3 seconds.

On a regular basis people tell me about their impressive achievements using AI. This post may come off as a rant, but that's not so much its intent, as it is to point out why we went from having very few AI experts, to having so many in so little time. Also to convey that most of these experts only seem experty because so few people know how to call them on their bull shit. So you converted 11 lines of python that would fit on a t-shirt to Java or C or C . You have mastered what a cross compiler can do in 3 seconds.

I have been looking to create this list for a while now. There are many people on quora who ask me how I started in the data science field. And so I wanted to create this reference. To be frank, when I first started learning it all looked very utopian and out of the world. The Andrew Ng course felt like black magic.

In June on /r/MachineLearning we learned of funding to a popular (and essential) Python project, are treated to a collection of machine learning cheat sheets, see how deep learning is done on premium cable television, read about Andre Karpathy's new job, and are introduced to a new machine learning "IDE." This is good news for the project. For the first time ever, NumPy -- a core project for the Python scientific computing stack -- has received grant funding. The proposal, "Improving NumPy for Better Data Science" will receive $645,020 from the Moore Foundation over 2 years, with the funding going to UC Berkeley Institute for Data Science. The principal investigator is Dr. Nathaniel Smith.

I have been looking to create this list for a while now. There are many people on quora who ask me how I started in the data science field. And so I wanted to create this reference. To be frank, when I first started learning it all looked very utopian and out of the world. The Andrew Ng course felt like black magic.

There are some excellent resources here. But, I thought, the more helpful approach might be a plan and hence am adding one more answer to this list. I am deliberately not giving a link as I want you to search through multiple sets. Create a deck of slides describing the business problem, ROI, current practices, their weakness etc. Mile stone 1: Creating a business context for a problem is a crucial step in becoming a practitioner. Congrats, you have done that!

I had the pleasure of joining an intimate dinner hosted by Fast Company (not pictured above) this week and for the 3rd time in as many days found myself embroiled in a conversation about AI and its impact on industry. It was a room full of smart people talking passionately about the robot apocalypse; in this case, through the lens of self-driving cars and its impact on the humans in the Taxi and Trucking industries. There were tons of great points around the table about timelines, and augmenting vs. replacing but the one forgone conclusion seemed to be that the continued rise of ever-more sophisticated industry means… And while folks brought up the possibility of a Universal Basic Income Tax, there was one thing I didn't hear from anyone. In fact, one thing that I haven't heard anywhere in the discussion around what we're going to do when AI really gets cranking in all of its commercial and industrial: Can we retrain all 3.5mm truck drivers and 250K taxi drivers in the US if their jobs largely go away over the next 10–20 years? Can we make some of them Road Performance quality control experts?

But, I thought, the more helpful approach might be a plan. Skills you need: Ability to take Excel/CSV data sets, pre-process and visualize; Build a model and Visualize the results. I am deliberately not giving a link as I want you to search through multiple sets. Create a deck of slides describing the business problem, ROI, current practices, their weakness etc. Mile stone 1: Creating a business context for a problem is a crucial step in becoming a practitioner. Congrats, you have done that!