Using Optuna to Optimize PyTorch Lightning Hyperparameters

From: https://medium.com/optuna/using-optuna-to-optimize-pytorch-lightning-hyperparameters-d9e04a481585 This post uses pytorch-lightning v0.6.0 (PyTorch v1.3.1)and optuna v1.1.0. PyTorch Lightning + Optuna! Optuna is a hyperparameter optimization framework applicable to machine learning frameworks and black-box optimization solvers. PyTorch Lightning provides a lightweight PyTorch wrapper for better scaling with less code. Combining the two of them allows for automatic tuning of hyperparameters to find the … Continue reading Using Optuna to Optimize PyTorch Lightning Hyperparameters →

Faster Video Processing in Python using Parallel Computing

From: https://towardsdatascience.com/faster-video-processing-in-python-using-parallel-computing-25da1ad4a01 If you want to process a number of video files, it might take a from minutes to hours, depending on the size of the video, frame count, and frame dimensions. How can we speed up video processing? Parallel processing is the answer! If you are processing images in batches, you can utilize the … Continue reading Faster Video Processing in Python using Parallel Computing →

Word Embeddings vs TF-IDF: Answering COVID-19 Questions

From: https://towardsdatascience.com/word-embeddings-vs-tf-idf-answering-covid-19-questions-703e3d99f783 A comparison of text similarity methods for answering COVID-19 questions. Dataset: CORD-19 Questions we are interested in: Data on potential risks factorsSmoking, pre-existing pulmonary diseaseCo-infections (determine whether co-existing respiratory/viral infections make the virus more transmissible or virulent) and other co-morbiditiesNeonates and pregnant womenSocio-economic and behavioral factors to understand the economic impact of the virus … Continue reading Word Embeddings vs TF-IDF: Answering COVID-19 Questions →

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 →

Build an Algorithmic Trading System

From: https://towardsdatascience.com/build-an-algorithmic-trading-system-a5b54de5379 After dozens of emails and DMs, it seemed appropriate to write a proper introduction to getting started with Algorithmic Trading. This guide should serve as a walkthrough for building your first Proof of Concept algorithmic trading system. Disclaimer: Luke is a Co-Founder of Spawner, a company whose tech is directly mentioned and used in … Continue reading Build an Algorithmic Trading System →

Predicting Pharmacokinetics with Deterministic Models and Bayesian Statistics

From: https://towardsdatascience.com/predicting-pharmacokinetics-with-deterministic-models-and-bayesian-statistics-e3822a28977b Introduction Pharmacokinetics Pharmacokinetics (PK) deals with the distribution and metabolism of xenobioticsin organisms. In drug development and clinical research, one encounters pharmacologists whose job it is to advise physicians the dose of drug(s) to provide to their patients. For this purpose, they often run simulations of so-called pharmacokinetic models which are deterministic, i.e. they consist of ordinary differential equationswith … Continue reading Predicting Pharmacokinetics with Deterministic Models and Bayesian Statistics →

Quantitative Analytics: Optimal Portfolio Allocation

From: https://lf0.com/post/portfolio-optimisation-model/portfolio-optimisation-r/ Introduction: The literature in portfolio optimisation has been around for decades. In this post I cover a number of traditional portfolio optimisation models. The general aim is to select a portfolio of assets out of a set of all possible portfolios being considered with a defined objective function. The data: The data is … Continue reading Quantitative Analytics: Optimal Portfolio Allocation →

Deep Learning based Super Resolution with OpenCV

From: https://towardsdatascience.com/deep-learning-based-super-resolution-with-opencv-4fd736678066 Table of Content Super ResolutionSteps to upscale images in OpenCVDifferent pre-trained modelsSamples of resultsNotes and references Super Resolution in OpenCV OpenCV is an open-source computer vision library that has an extensive collection of great algorithms. Since one of the latest mergers, OpenCV contains an easy-to-use interface for implementing Super Resolution (SR) based on … Continue reading Deep Learning based Super Resolution with OpenCV →

Tableau-like Drag and Drop GUI Visualization in R

From: https://towardsdatascience.com/tableau-esque-drag-and-drop-gui-visualization-in-r-901ee9f2fe3f One of the the few things that Self-service Data Visualization tools like Tableau and Qlik offer that sophisticated Data Science Languages like R and Python do not offer is — The Drag and Drop GUI to create Visualizations. The flexibility with which you can simply drag and drop your Dimensions and Metrics is so … Continue reading Tableau-like Drag and Drop GUI Visualization in R →

Attention Mechanism in Deep Learning : Simplified

From: https://medium.com/@prakhargannu/attention-mechanism-in-deep-learning-simplified-d6a5830a079d What exactly is the attention mechanism? Look at the image below and answer me, what is the color of the soccer ball? Also, which Georgetown player, the guys in white, is wearing the captaincy band? [Source] When you were trying to figure out answers to the questions above, did your mind do this … Continue reading Attention Mechanism in Deep Learning : Simplified →