From: https://pbpython.com/monte-carlo.html Introduction There are many sophisticated models people can build for solving a forecasting problem. However, they frequently stick to simple Excel models based on average historical values, intuition and some high level domain-specific heuristics. This approach may be precise enough for the problem at hand but there are alternatives that can add more … Continue reading Monte Carlo Simulation with Python
From: https://medium.com/neuralspace/bayesian-convolutional-neural-networks-with-bayes-by-backprop-c84dcaaf086e So far, we have elaborated how Bayes by Backprop works on a simple feedforward neural network. In this post, I will explain how you can apply exactly this framework to any convolutional neural network (CNN) architecture you like. You might have seen Gal’s & Ghahramani’s (2015) publication of a Bayesian CNN, but that’s an entirely different approach … Continue reading Bayesian Convolutional Neural Networks with Bayes by Backprop
From: https://medium.com/tensorflow/predicting-known-unknowns-with-tensorflow-probability-industrial-ai-part-2-2fbd3522ebda Posted by Venkatesh Rajagopalan, Director Data Science & Analytics and Arun Subramaniyan, VP Data Science & Analytics at BHGE Digital In the first blog of this series, we presented our analytics philosophy of combining domain knowledge, probabilistic methods, traditional machine learning (ML) and deep learning techniques to solve some of the hardest problems in the industrial world. … Continue reading Predicting Known Unknowns with TensorFlow Probability — Industrial AI, Part 2
From: https://medium.com/tensorflow/industrial-ai-bhges-physics-based-probabilistic-deep-learning-using-tensorflow-probability-5f215c791863 By Arun Subramaniyan, VP Data Science & Analytics at BHGE Digital Baker Hughes, a GE Company (BHGE), is the world’s leading fullstream oil and gas company with a mission to find better ways to deliver energy to the world. The BHGE Digital team develops enterprise grade, AI-driven, SaaS solutions to improve efficiency and reduce non-productive time … Continue reading Industrial AI: BHGE’s Physics-based, Probabilistic Deep Learning Using TensorFlow Probability — Part 1
Telecommunication From: https://www.math3ma.com/blog/matrices-probability-graphs Today I'd like to share an idea. It's a very simple idea. It's not fancy and it's certainly not new. In fact, I'm sure many of you have thought about it already. But if you haven't—and even if you have!—I hope you'll take a few minutes to enjoy it with me. Here's the idea: … Continue reading Viewing Matrices & Probability as Graphs
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