Has been a long time since the last time I wrote on this blog. Many things are changed in my life since then. My journey at Curcuma wasn’t so happy as I hoped and after 6 months of hard work I left and joined the amazing team at Ernest.ai.

Ernest: Your financial coach

We are building a smart chatbot to help people managing personal finance. Currently, we are in closed beta (here you can sign.up to the waiting list). Team is distributed between London and Milan. Here is a beautiful photo taken during last meeting in London a couple of months ago.

The Ernest team, WeWork Old Street, London, December 2016.

During the last year, career path switch and parenting took all my time and chances to write vanished. Anyhow experiences I did allow me to learn a lot about Machine Learning, Artificial Intelligence, Conversational Interfaces, Chatbots, Functional and Reactive programming and many other exciting topics and now, the beginning of 2017, could be the right time to restart giving back to the community.

See you on this feed 😉


Deep Learning is a trending buzzword in the Machine Learning environment. All the major players in Silicon Valley are heavily investing in these topics and US universities are improving their courses offer.

I’m really interested in artificial intelligence both for fun and for work and I spent a few hours in the last weeks searching for best MOOCs about this topic. I found only a few courses but they are from the most notable figures in Deep Learning and Neural Networks environment.

Machine Learning
Stanford University on Coursera, Andrew Ng

Andrew Ng is Chief Scientist at Baidu Research since 2015, founder of Coursera and Machine Learning lecturer at Stanford University. He also founded the Google Brain project in 2011. His Machine Learning (CS229a) course at Stanford is quite mythical and, obviously, was my starting point.


Machine Learning, Coursera

Neural Networks for Machine Learning
University of Toronto on Coursera, Geoffrey Hinton

Geoffrey Hinton is working at Google (probably on Google Brain) since 2013 when Google acquire his company DNNResearch Inc. He is a cognitive psychologist most noted for his work on artificial neural networks. His Coursera course on Neural Networks is related to 2012 but seem to be one of the best resource about these topics.


Neural Networks for Machine Learning, Coursera

Deep Learning (2015)
New York University on TechTalks, Yann LeCun (videos on techtalks.tv)

In 2013 LeCun became the first director of Facebook AI Research. He is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNN), and is a founding father of convolutional nets. 2015 Deep Learning course at NYU is the last course about this topic hold by him.

Yann LeCun. CIFAR NCAP pre-NIPS' Workshop. Photo: Josh Valcarcel/WIRED

Yann LeCun. CIFAR NCAP pre-NIPS’ Workshop. Photo: Josh Valcarcel/WIRED

Big Data, Large Scale Machine Learning
New York University on TechTalks, John Langford and Yann LeCun

Another interesting course about Machine Learning hold by LeCun and John Langford, researcher at Yahoo Research, Microsoft Research and IBM’s Watson Research Center.


John Langford, NYU

Deep Learning Courses
NVIDIA Accelerated Computing

This is not a college course. NVIDIA was one of the most important graphic board manufacturer in the early 2000s and now, with the experience of massive parallel computer on GPUs, is heavily investing in Deep Learning. This course is focused on usage of GPUs on most common deep learning framework: DIGITS, Caffe, Theano and Torch.


Deep Learning Courses, NVIDIA

Mastering Apache Spark
Mike Frampton, Packt Publishing

Last summer I had the opportunity to collaborate in review of this title. Chapter about MLlib contains a useful introduction to Artificial Neural Networks on Spark. Implementation seems still young but is already possible to distribute the network over a Spark cluster.


Mastering Apache Spark

[UPDATE 2016-01-31]

Deep Learning 
Vincent Vanhoucke, Google, Udacity

Google, a few days ago, releases on Udacity a Deep Learning course focused on TensorFlow, its deep learning tool. It’s the first course officially sponsored by a big companym is free and seems a great introduction. Thanks to Piotr Chromiec for pointing 🙂


I few days ago I have been at Codemotion in Milan and I had the opportunity to discover some insights about technologies used by two of our main competitor in Italy: BlogMeter and Datalytics. It’s quite interesting because, also if technical challenges are almost the same, each company use a differente approach with a different stack.


Datalytics a is relatively new company founded 4 months ago. They had a desk at Codemotion to show theirs products and recruit new people. I chatted with Marco Caruso, the CTO (who probably didn’t know who I am, sorry Marco, I just wanted to avoid hostility 😉 ), about technologies they use and developer profile they were looking for. Requires skills was:

Their tech team is composed by 4 developers (including the CTO) and main products are: Datalytics Monitoring™ (a sort of statistical dashboard that shows buzz stats in real time) and Datalytics Engage™ (a real time analytics dashboard for live events). I have no technical insights about how they systems works but I can guess some details inferring them from the buzz words they use.

Supported sources are Twitter, Facebook (only public data), Instagram, Youtube, Vine (logos are on their website) and probably Pinterest.

They use DataSift as data source in addition to standard APIs. I suppose their processing pipeline uses Storm to manage streaming input, maybe with an importing layer before. Data is crunched using Hadoop and Java and results are stored on MongoDB (Massimo Brignoli, Italian MongoDB evangelist, advertise their company during his presentation so I suppose they largely use it).

Node.js should be used for frontend. Is fast enough for near real time application (also using websockets) and play really well both with Angular.js and MongoDB (the MEAN stack). D3.js is obviously the only choice for complex dynamic charts.

I’m not so happy when I discover a new competitor in our market segment. Competition gets harder and this is not fun. Anyway guys at Datalytics seems smart (and nice) and compete with them would be a pleasure and will push me to do my best.

Now I’m curios to know if Datalytics is monitoring buzz on the web around its company name. I’m going to tweet about this article using #Datalytics hashtag. If you find this article please tweet me “Yes, we found it bwahaha” 😛

[UPDATE 2014-12-27 21:18 CET]

@DatalyticsIT favorite my tweet on December 1st. This probably means they found my article but the didn’t read it! 😀