Deep Learning/Machine Learning Technical Interest Group (TIG)

Applying new analytics, neural networks, computational approaches using structured and unstructured data, and also training neural networks with supervised and unsupervised algorithms. Chaired by Patrick Ng and Andrew Munoz.
Deep Learning - Machine Learning TIG
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0 Replies and 355 Views Read This First!      355  0 Started by  Bogdan Michka Welcome to AAPG N.E.T., an online space where you can Network, Engage and Talk. Please complete the following steps before you begin interacting with the discussion boards: 1. Take time to review your profile and privacy settings, paying attention to your display name and photo, as they will be used to identify your posts. 2. Read the Discussion Boards FAQ page to learn the rules and get familiar with the features: — pa...
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09 May 2017 03:37 PM
0 Replies and 12 Views Energy in Data 2021 - Altogether Immersive and Interactive Experience.  12  0 Started by  Patrick Ng AAPG-SEG-SPE kicks off 2021 with a all-digital immersive and interactive joint event online. We have heard from AI/ML workshops over 2019-2020 that members express interest in creating a community to advance knowledge and share experience on their digital journey. EiD 2021 has taken the first step, and to build out the community, it is only a click away. Go online and register today.
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12 Jan 2021 02:52 PM
0 Replies and 50 Views AlphaFold 2.0  50  0 Started by  Andrew Munoz DeepMind has made some major upgrades to AlphaFold because they are now able to successfully predict protein folding. This is going to be a major leap for using predictive analytics for disease research. I think it's great for everyone to know and understand leaps in deep learning in all fields. Naser Tamini has a great Medium article simplifying the result and explaining the solution: I hope you enjoy! Andrew
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03 Dec 2020 06:05 PM
0 Replies and 117 Views Highlights on Tackle the Issues - Where should Machine Learning go (and not go)?  117  0 Started by  Patrick Ng Observations - Dr. Lian and speaker Ball showed in examples simple architecture, random forest (RF) and support vector machine (SVM) work well in many cases (some as good as deep learning models). Q1: what does that tell us about ML model - simplicity vs complexity, and the direction Recap - much has to do with data / sampling. If we have lots of data, deep learning will do well. When we have limited data, RF and SVM may perform better (in supervised and unsupervis...
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02 Oct 2020 05:21 PM
0 Replies and 68 Views Business Acumen, Soft Skills and Machine Learning  68  0 Started by  Patrick Ng In the opening Michel T. Halbouty Lecture: Business Acumen and Soft Skills in an Everchanging Exploration World Day: Duval talked about “soft skills” now considered all the more important since the “engineering” preparation of a prospective deal and the related technologies are and will be more and more dependent on data analytics, advanced approaches involving AI, machine learning, etc. Takeaway - two ML questions were raised in Chat, worthy of recap here. Q1: if you have physics based mo...
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02 Oct 2020 01:53 PM
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