When Bayes, Ockham, and Shannon come together to define machine learning

From: https://towardsdatascience.com/when-bayes-ockham-and-shannon-come-together-to-define-machine-learning-96422729a1ad Acknowledgments Thanks to my CS7641 class at Georgia Tech in my MS Analytics program, where I discovered this concept and was inspired to write about it. Thanks to Matthew Mayo for editing and re-publishing this in KDnuggets. Introduction It is somewhat surprising that among all the high-flying buzzwords of machine learning, we don’t hear much about the one phrase … Continue reading When Bayes, Ockham, and Shannon come together to define machine learning

Generative vs Discriminative Probabilistic Graphical Models

From: https://towardsdatascience.com/generative-vs-2528de43a836 Generative and discriminative models are widely used machine learning models. For example, Logistic Regression, Support Vector Machine and Conditional Random Fields are popular discriminative models; Naive Bayes, Bayesian Networks and Hidden Markov models are commonly used generative models. Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains … Continue reading Generative vs Discriminative Probabilistic Graphical Models

Product Price Prediction: A Tidy Hyperparameter Tuning and Cross Validation Tutorial

From: https://www.business-science.io/code-tools/2020/01/21/hyperparamater-tune-product-price-prediction.html Product price estimation and prediction is one of the skills I teach frequently - It's a great way to analyze competitor product information, your own company's product data, and develop key insights into which product features influence product prices. Learn how to model product car prices and calculate depreciation curves using the brand new tune package for Hyperparameter Tuning Machine Learning Models. This is … Continue reading Product Price Prediction: A Tidy Hyperparameter Tuning and Cross Validation Tutorial

Accelerating TSNE with GPUs: From hours to seconds

From: https://medium.com/rapids-ai/tsne-with-gpus-hours-to-seconds-9d9c17c941db Figure 1. cuML TSNE on MNIST Fashion takes 3 seconds. Scikit-Learn takes 1 hour. TSNE (T-Distributed Stochastic Neighbor Embedding) is a popular unsupervised dimensionality reduction algorithm that finds uses as varied as neurology, image similarity, and visualizing neural networks. Unfortunately, its biggest drawback has been the long processing times in most available implementations. RAPIDS now provides fast GPU-accelerated TSNE, … Continue reading Accelerating TSNE with GPUs: From hours to seconds

List of Data Science and Machine Learning GitHub Repositories to Try in 2019

Here is the list of selected Data Science and Machine Learning GitHub Repositories to Try in 2019, from https://www.marktechpost.com/2019/05/28/list-of-data-science-and-machine-learning-github-repositories-to-try-in-2019/ Paper with Code Facebook’s Detectron Training a Model on the ImageNet Dataset in 18 Minutes NVIDIA’s vid2vid Technique Facebook’s DensePose Pytorch-EverybodyDanceNow Deep learning object detection Deep-painterly-harmonization Image OutPainting ML.NET TensorFlow.js PyTorch 1.0 GANimation GANDissect WaveGlow VisualDL … Continue reading List of Data Science and Machine Learning GitHub Repositories to Try in 2019