A lightweight command line interface library for creating commands

hype A lightweight command line interface library for creating cli commands. Hype CLI is an open source framework use for building command line applications easirer for cli applications that required different type of commands. It also comes with alot of different features that you may want to check out. Hype CLI was mainly built for Anglo ( a modern lightweight web framework for python 3. ). Because of Hype CLI’s capability it becomes easier to build command-line application. You can […]

August 7, 2021 processing

A Python package to create, run, and post-process MODFLOW-based models

flopy FloPy includes support for MODFLOW 6, MODFLOW-2005, MODFLOW-NWT, MODFLOW-USG, and MODFLOW-2000. Other supported MODFLOW-based models include MODPATH (version 6 and 7), MT3DMS, MT3D-USGS, and SEAWAT. For general modeling issues, please consult a modeling forum, such as the MODFLOW Users Group. Other MODFLOW resources are listed in the MODFLOW Resources section. Installation FloPy requires Python 3.5 (or higher) and NumPy 1.9 (or higher). Dependencies for optional FloPy methods are summarized here. To install FloPy type: conda install -c conda-forge flopy […]

August 7, 2021 Scripts

A Python script that contains various functions

element_search with Selenium Just to mention, I’m a beginner to all this, so it it’s very possible to make some mistakes The idea is to create a Python script that contains various functions, that with the help of the Selenium library, searches for web elements, raising no errors, and in the same time writes automatically into a log file what is being searched and if the element was found. Important note: * you should already have selenium installed * along […]

August 7, 2021 Advanced, Machine Learning, NLP, Project, Python, Text, Unstructured Data

Email Spam Detection – A Comparative Analysis of 4 Machine Learning Models

This article was published as a part of the Data Science Blogathon Introduction This article aims to compare four different deep learning and machine learning algorithms to build a spam detector and evaluate their performances. The dataset we used was from a shuffled sample of email subjects and bodies containing both spam and ham emails in numerous proportions, which we converted into lemmas. Email Spam Detection is one of the most effective projects of Deep learning but this is often also […]

August 6, 2021 Machine Learning

Progressive Growing of GANs inference in PyTorch with CelebA training snapshot

prog_gans_pytorch_inference This is an inference sample written in PyTorch of the original Theano/Lasagne code. I recreated the network as described in the paper of Karras et al. Since some layers seemed to be missing in PyTorch, these were implemented as well. The network and the layers can be found in model.py. For the demo, a 100-celeb-hq-1024×1024-ours snapshot was used, which was made publicly available by the authors. Since I couldn’t find any model converter between Theano/Lasagne and PyTorch, I used […]

August 6, 2021 Transformer

An Introduction to Deep Learning for the Physical Layer

radio-transformer-networks An Introduction to Deep Learning for the Physical Layer An usable PyTorch implementation of the noisy autoencoder infrastructure in the paper “An Introduction to Deep Learning for the Physical Layer” by Kenta Iwasaki on behalf of Gram.AI. Overall a fun experiment for constructing a communications system for the physical layer with transmitters/receivers in which the transmitter efficiently encodes a signal in a way such that the receiver can still, with minimal error, decode this encoded signal despite being inflicted […]

August 6, 2021 PyTorch

PyTorch implementations of neural network models for keyword spotting

Honk: CNNs for Keyword Spotting Honk is a PyTorch reimplementation of Google’s TensorFlow convolutional neural networks for keyword spotting, which accompanies the recent release of their Speech Commands Dataset. For more details, please consult our writeup: Raphael Tang, Jimmy Lin. Honk: A PyTorch Reimplementation of Convolutional Neural Networks for Keyword Spotting. arXiv:1710.06554, October 2017. Raphael Tang, Jimmy Lin. Deep Residual Learning for Small-Footprint Keyword Spotting. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 5479-5483. […]

August 6, 2021 Deep Learning

Deep CORAL: Correlation Alignment for Deep Domain Adaptation

Deep CORAL A PyTorch implementation of ‘Deep CORAL: Correlation Alignment for Deep Domain Adaptation. B Sun, K Saenko, ECCV 2016’ Deep CORAL can learn a nonlinear transformation that aligns correlations of layer activations in deep neural networks (Deep CORAL). My implementation result (Task Amazon -> Webcam): Requirement Usage Unzip dataset in dataset/office31.tar.gz Run python3 main.py GitHub https://github.com/SSARCandy/DeepCORAL

August 6, 2021 PyTorch

A PyTorch toolkit for 2D Human Pose Estimation

PyTorch-Pose PyTorch-Pose is a PyTorch implementation of the general pipeline for 2D single human pose estimation. The aim is to provide the interface of the training/inference/evaluation, and the dataloader with various data augmentation options for the most popular human pose databases (e.g., the MPII human pose, LSP and FLIC). Some codes for data preparation and augmentation are brought from the Stacked hourglass network. Thanks to the original author. Update: this repository is compatible with PyTorch 0.4.1/1.0 now! Features Multi-thread data […]

August 6, 2021 PyTorch

Language Emergence in Multi Agent Dialog

Language Emergence in Multi Agent Dialog Code for the Paper Natural Language Does Not Emerge ‘Naturally’ in Multi-Agent Dialog Satwik Kottur, José M. F. Moura, Stefan Lee, Dhruv Batra EMNLP 2017 (Best Short Paper) If you find this code useful, please consider citing the original work by authors: @inproceedings{visdial, title = {{N}atural {L}anguage {D}oes {N}ot {E}merge ‘{N}aturally’ in {M}ulti-{A}gent {D}ialog}, author = {Satwik Kottur and Jos’e M.F. Moura and Stefan Lee and Dhruv Batra}, journal = {CoRR}, volume = {abs/1706.08502}, […]

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