A Collection of 10 Data Visualizations You Must See

From: https://www.analyticsvidhya.com/blog/2018/01/collection-data-visualizations-you-must-see/ Introduction Writing codes is fun. Creating models with them is even more intriguing. But things start getting tricky when it comes to presenting our work to a non-technical person. This is where visualizations comes in. They are one of the best ways of telling a story with data. In this article, we look … Continue reading A Collection of 10 Data Visualizations You Must See →

Object-oriented programming for data scientists: Build your ML estimator

From: https://towardsdatascience.com/object-oriented-programming-for-data-scientists-build-your-ml-estimator-7da416751f64 Implement some of the core OOP principles in a machine learning context by building your own Scikit-learn-like estimator, and making it better. What is the problem? Data scientists often come from a background which is quite far removed from traditional computer science/software engineering — physics, biology, statistics, economics, electrical engineering, etc. Source: “Where do … Continue reading Object-oriented programming for data scientists: Build your ML estimator →

Monte Carlo Simulation with Python

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 →

Bayesian Convolutional Neural Networks with Bayes by Backprop

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 →

Modeling “Unknown Unknowns” with TensorFlow Probability — Industrial AI, Part 3

From: https://medium.com/tensorflow/modeling-unknown-unknowns-with-tensorflow-probability-industrial-ai-part-3-52146cd0201a Posted by Venkatesh Rajagopalan, Director Data Science & Analytics; Mahadevan Balasubramaniam, Principal Data Scientist; and Arun Subramaniyan, VP Data Science & Analytics at BHGE Digital We believe in a slightly modified version of George Box’s famous comment: “All models are wrong, some are useful” for a short period of time. Irrespective of how sophisticated a model … Continue reading Modeling “Unknown Unknowns” with TensorFlow Probability — Industrial AI, Part 3 →

Predicting Known Unknowns with TensorFlow Probability — Industrial AI, Part 2

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 →

Industrial AI: BHGE’s Physics-based, Probabilistic Deep Learning Using TensorFlow Probability — Part 1

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 →

A Primer on Deep Learning in Genomics

From: https://colab.research.google.com/drive/17E4h5aAOioh5DiTo7MZg4hpL6Z_0FyWr#scrollTo=eiiwjw4yhX0P Deep Learning in Genomics Primer (Tutorial) This tutorial is a supplement to the manuscript, A Primer on Deep Learning in Genomics (Nature Genetics, 2018) by James Zou, Mikael Huss, Abubakar Abid, Pejman Mohammadi, Ali Torkamani & Amalio Telentil. Read the accompanying paper here. Paper: https://drive.google.com/file/d/1441g10nACGWVfMeMU4aYxaODYC_bl2Oi/view?usp=sharing If you have any questions or feedback regarding this tutorial, please … Continue reading A Primer on Deep Learning in Genomics →

Breast cancer classification with Keras and Deep Learning

From: https://www.pyimagesearch.com/2019/02/18/breast-cancer-classification-with-keras-and-deep-learning/ In this tutorial, you will learn how to train a Keras deep learning model to predict breast cancer in breast histology images. Back 2012-2013 I was working for the National Institutes of Health (NIH) and the National Cancer Institute (NCI) to develop a suite of image processing and machine learning algorithms to automatically analyze … Continue reading Breast cancer classification with Keras and Deep Learning →

Mastering Fast Gradient Boosting on Google Colaboratory with free GPU

On the photo NVIDIA K80 GPU, https://www.nvidia.com/ru-ru/data-center/tesla-k80/ Gradient Boosting on Decision Trees (GBDT) is a state-of-the-art Machine Learning tool for working with heterogeneous or structured data. When working with data, the choice of the perfect algorithm depends highly on the type of data. For homogeneous data, like images, sound or text, the best solution is neural networks. … Continue reading Mastering Fast Gradient Boosting on Google Colaboratory with free GPU →