June 28, 2021 Emulation

An emulated LED scoreboard for Major League Baseball in python

mlb-led-scoreboard-emulated An LED scoreboard for Major League Baseball. Displays a live scoreboard for your team’s game on that day. Be sure to check out the original repo, mlb-led-scoreboard, if you’d like to run this on a Rasperry Pi LED panel! Currently supported boards: 32×32 64×32 64×64 128×32 128×64 If you’d like to see support for another set of board dimensions, file an issue! Screenshots Installation Requirements You need Git for cloning this repo and PIP for installing the scoreboard software. […]

June 28, 2021 RESTful API

Restful Api developed with Flask using Prometheus and Grafana for monitoring

Hephaestus Restful Api developed with Flask using Prometheus and Grafana for monitoring and containerization with Docker. In Greek mythology, Hephaestus was either the son of Zeus and Hera or he was Hera’s parthenogenous child. … As a smithing god, Hephaestus made all the weapons of the gods in Olympus. He served as the blacksmith of the gods, and was worshipped in the manufacturing and industrial centres of Greece, particularly Athens. Get Started :rocket: An Restful Api project developed with Flask. […]

June 28, 2021 Simulation

Massively parallel rigidbody physics simulation on accelerator hardware

BRAX Brax is a differentiable physics engine that simulates environments made up of rigid bodies, joints, and actuators. It’s also a suite of learning algorithms to train agents to operate in these environments (PPO, SAC, evolutionary strategy, and direct trajectory optimization are implemented). Brax is written in JAX and is designed for use on acceleration hardware. It is both efficient for single-core training, and scalable to massively parallel simulation, without the need for pesky datacenters. Some policies trained via Brax. […]

June 28, 2021 Terminal

A FOSS alternative to Bloomberg Terminal

Gamestonk Terminal The next best thing after Bloomberg Terminal. How it started: Gamestonk Terminal is an awesome stock and crypto market terminal that has been developed for fun, while I saw my GME shares tanking. But hey, I like the stock 💎🙌. How it’s going: Gamestonk Terminal provides a modern Python-based integrated environment for investment research, that allows the average joe retail trader to leverage state-of-the-art Data Science and Machine Learning technologies. As a modern Python-based environment, GamestonkTerminal opens access […]

June 28, 2021 Deep Learning

A unified and flexible and comprehensive traffic prediction library

LibTraffic（阡陌） LibTraffic is a unified, flexible and comprehensive traffic prediction library, which provides researchers with a credibly experimental tool and a convenient development framework. Our library is implemented based on PyTorch, and includes all the necessary steps or components related to traffic prediction into a systematic pipeline. LibTraffic currently supports the following tasks: Traffic State Prediction Traffic Flow Prediction Traffic Speed Prediction On-Demand Service Prediction Trajectory Next-Location Prediction Features Unified: LibTraffic builds a systematic pipeline to implement, use and evaluate […]

June 28, 2021 Machine Learning

Objective of the repository is to learn and build machine learning models using Pytorch

30 Days Of Machine Learning Using Pytorch Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch List of Algorithms Covered 📌 Day 1 – Linear Regression 📌 Day 2 – Logistic Regression 📌 Day 3 – Decision Tree 📌 Day 4 – KMeans Clustering 📌 Day 5 – Naive Bayes 📌 Day 6 – K Nearest Neighbour (KNN) 📌 Day 7 – Support Vector Machine 📌 Day 8 – Tf-Idf Model 📌 […]

June 28, 2021 Visualization

Creating publication-quality figures with Matplotlib

matplotlib_for_papers Handout for the tutorial “Creating publication-quality figures with matplotlib” This repository contains the handout (and the source of the handout) for the tutorial “Creating publication-quality with Python and Matplotlib”, given at the Alife 2014 conference. Contributions are welcomed: feel free to clone and send pull requests. Examples of figures: Reference Tonelli, Paul, and Jean-Baptiste Mouret. “On the relationships between generative encodings, regularity, and learning abilities when evolving plastic artificial neural networks.” PloS one 8.11 (2013): e79138. Reference Clune*, Jeff, […]

June 28, 2021 Python

Reverse Python Lists: Beyond .reverse() and reversed()

Sometimes you need to process Python lists starting from the last element down to the first—in other words, in reverse order. In general, there are two main challenges related to working with lists in reverse: To meet the first challenge, you can use either .reverse() or a loop that swaps items by index. For the second, you can use reversed() or a slicing operation. In the next sections, you’ll learn about different ways to accomplish both in your code. Reversing […]

June 28, 2021 Advanced, NLP, Text

Part 18: Step by Step Guide to Master NLP – Topic Modelling using LDA (Probabilistic Approach)

This article was published as a part of the Data Science Blogathon Introduction This article is part of an ongoing blog series on Natural Language Processing (NLP). In the previous part of this series, we completed our discussion on pLSA, which is a probabilistic framework for Topic Modelling. But we have seen some of the limitations of pLSA, so to resolve those limitations LDA comes into the picture. So, In this article, we will discuss the probabilistic or Bayesian approach to […]

June 28, 2021 NLP, Terminology

Issue #136 – Neural Machine Translation without Embeddings

28 Jun21 Issue #136 – Neural Machine Translation without Embeddings Author: Dr. Jingyi Han, Machine Translation Scientist @ Language Weaver Introduction Nowadays, Byte Pair Encoding (BPE) has become one of the most commonly used tokenization strategies due to its universality and effectiveness in handling rare words. Although many previous works show that subword models with embedding layers in general achieve more stable and competitive results in neural machine translation (NMT), character-based (see issue #60) and Byte-based subword (see issue #64) […]

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