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 πŸ’ŽπŸ™Œ. 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 […]

Read more

A unified and flexible and comprehensive traffic prediction library

LibTrafficοΌˆι˜‘ι™ŒοΌ‰ 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 […]

Read more

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 πŸ“Œ Day 1 – Linear Regression πŸ“Œ Day 2 – Logistic Regression πŸ“Œ Day 3 – Decision Tree πŸ“Œ Day 4 – KMeans Clustering πŸ“Œ Day 5 – Naive Bayes πŸ“Œ Day 6 – K Nearest Neighbour (KNN) πŸ“Œ Day 7 – Support Vector Machine πŸ“Œ Day 8 – Tf-Idf Model πŸ“Œ […]

Read more

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, […]

Read more

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

Sometimes you need to process Python lists starting from the last element down to the firstβ€”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 […]

Read more

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

This article was published as a part of theΒ Data Science Blogathon Introduction This article is part of an ongoing blog series on Natural Language Processing (NLP). Β 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 […]

Read more

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) […]

Read more

109 Python Problems for CCPS 109

PythonProblems This repository contains the problem specifications, the automated tester and the necessary data files for the graded lab problems for the course CCPS 109 Computer Science I, as taught by Ilkka Kokkarinen for the Chang School of Continuing Education, Ryerson University, Toronto, Canada. Write all your functions one by one into the same file labs109.py so that the acceptance tester script tester109.py can find them. This tester will execute the automated acceptance tests precisely for the functions implemented in […]

Read more

Declarative CLIs with argparse and dataclasses

argparse_dataclass Declarative CLIs with argparse and dataclasses. Features Features marked with a βœ“ are currently implemented; features marked with a ⊘ are not yet implemented. [βœ“] Positional arguments [βœ“] Boolean flags [βœ“] Integer, string, float, and other simple types as arguments [βœ“] Default values [βœ“] Arguments with a finite set of choices [⊘] Subcommands [⊘] Mutually exclusive groups Examples Using dataclass decorator >>> from argparse_dataclass import dataclass >>> @dataclass … class Options: … x: int = 42 … y: bool […]

Read more

Proper project management with Taskwarrior in vim

Taskwiki Proper project management in vim. Standing on the shoulders of vimwiki and Taskwarrior Installation Make sure you satisfy the requirements Vim 7.4 or newer, with +python or +python3 (NeoVim is also supported) Vimwiki (the dev branch) git clone https://github.com/vimwiki/vimwiki ~/.vim/bundle/ –branch dev Taskwarrior (version 2.4.0 or newer), install either from sources or using your package manager sudo dnf install task tasklib (the develop branch), Python library for Taskwarrior. sudo pip3 install –upgrade -r requirements.txt For neovim users: Note that […]

Read more
1 593 594 595 596 597 928