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Deep Learning / Machine Learning User Group Nürnberg

Stammtisch in Nürnberg und Umgebung um die Themen Deep Learning, Machine Learning und Tools/Frameworks wie Theano, Torch, TensorFlow, Keras

Andras Molnar The Google Brain Team was quite busy in 2017
Matt Reaney Data Science, Machine Learning and AI Conferences 2018
Mark Keaton Introduction
Hi everyone and Happy New Year 2018🙋‍♂️
Thank you for adding me. Let me introduce us shortly.
We are an independent group of senior devs/freelancers. Four of us are Ph.D.'s in Machine Learning and Mathematics. We are focused on Machine Learning - Pattern recognition, Algo trading, NLP, Artificial Intelligence in FinTech and HealthTech, Blockchain, cryptocurrencies, and chatbots.
Our Stack:
Clojure, ClojureScript, Blockchain, Python (TensorFlow), AWS, JS, Node.Js, PHP, Angular, C# (.NET Core), PostgreSQL, ReactJS, Scala, Presto, Docker, Linux and many others.
Here are some projects we are working on:
Stealth startup - Machine Learning platform for forex trading
Stealth startup - Pathological image recognition to diagnose Hirschsprung disease
We have some free capacity now and able to help, If someones looking for Machine Learning developers:) Ideally, we are looking for long-term cooperation. We even teach at universities. Also would like to meet people with the same interest to share experience or knowledge.
Send a message If you would like to chat a bit:)
Andras Molnar A new Deep Learning course from Andrew Ng is now online on Coursera
Andras Molnar
Ein weiterer Kommentar
Letzter Kommentar:
Nur für XING Mitglieder sichtbar
Arvind Nagaraj: Thoughts after taking the courses
Things I liked in this course:
1. Facts are pretty much laid out bare — All uncertainties & ambiguities are periodically eliminated
2. Andrew stresses on the engineering aspects of deep learning and provides plenty of practical tips to save time and money — the third course in the DL specialization felt incredibly useful for my role as an architect leading engineering teams.
3. Jargon is handled well. Andrew explains that an empirical process = trial & error — He is brutally honest about the reality of designing and training deep nets. At some point I felt he might have as well just called Deep Learning as glorified curve-fitting
4. Squashes all hype around DL and AI — Andrew makes restrained, careful comments about proliferation of AI hype in the mainstream media and by the end of the course it is pretty clear that DL is nothing like the terminator.
5.Wonderful boilerplate code that just works out of the box!
6. Excellent course structure.
7. Nice, consistent and useful notation. Andrew strives to establish a fresh nomenclature for neural nets and I feel he could be quite successful in this endeavor.
8. Style of teaching that is unique to Andrew and carries over from ML — I could feel the same excitement I felt in 2013 when I took his original ML course.
9.The interviews with deep learning heroes are refreshing — It is motivating and fun to hear personal stories and anecdotes.
Nur für XING Mitglieder sichtbar Great article about 'Artistic Style Transfer'
This article is about Artistic Style Transfer or you can call it Neural Style Transfer too. It is interesting to know that deep learning can make some magical things with images. So, I’ll try to give you a better understanding of this concept and how it works.
See also Week 4 (Neural style transfer) of Andrew Ng's Coursera Course 'Convolutional Neural Networks'


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Über die Gruppe "Deep Learning / Machine Learning User Group Nürnberg"

  • Gegründet: 12.10.2016
  • Mitglieder: 294
  • Sichtbarkeit: offen
  • Beiträge: 151
  • Kommentare: 35