Month: April 2021

Python

Learn Text Classification With Python and Keras

Imagine you could know the mood of the people on the Internet. Maybe you are not interested in its entirety, but only if people are today happy on your favorite social media platform. After this course, you’ll be equipped to do this. While doing this, you will get a grasp of current advancements of (deep) neural networks and how they can be applied to text. Reading the mood from text with machine learning is called sentiment analysis, and it is […]

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Course

How TensorFlow Works?

Tensor Flow permits the subsequent: Tensor Flow helps you to deploy computation to as a minimum one or extra CPUs or GPUs in a computing tool, server, or mobile device in a completely easy manner. This way the matters may be completed very speedy. Tensor Flow lets you specific your computation as a statistics glide graph. Tensor Flow helps you to visualize the graph using the in-constructed tensor board. You can test and debug the graph very without difficulty. Tensor […]

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Machine Learning

A tutorial on training a DarkNet YOLOv4 model for the CrowdHuman dataset

YOLOv4 CrowdHuman Tutorial This is a tutorial demonstrating how to train a YOLOv4 people detector using Darknet and the CrowdHuman dataset. Setup If you are going to train the model on Google Colab, you could skip this section and jump straight to Training on Google Colab. Otherwise, to run training locally, you need to have a x86_64 PC with a decent GPU. For example, I mainly test the code in this repository using a desktop PC with: NVIDIA GeForce RTX […]

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Tool

A toolbox that allows to train and evaluate deep learning models

Ludwig Ludwig is a toolbox that allows users to train and test deep learning models without the need to write code. It is built on top of TensorFlow. To train a model you need to provide is a file containing your data, a list of columns to use as inputs, and a list of columns to use as outputs, Ludwig will do the rest. Simple commands can be used to train models both locally and in a distributed way, and […]

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Machine Learning

A Python package for multiply imputing missing data using deep learning methods

MIDASpy MIDASpy is a Python package for multiply imputing missing data using deep learning methods. The MIDASpy algorithm offers significant accuracy and efficiency advantages over other multiple imputation strategies, particularly when applied to large datasets with complex features. In addition to implementing the algorithm, the package contains functions for processing data before and after model training, running imputation model diagnostics, generating multiple completed datasets, and estimating regression models on these datasets. Background and suggested citation For more information on MIDAS, […]

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Command-line Tools

Python3 library for multimedia functions at the command terminal

TERMINEDIA This is a Python library allowing using a text-terminal as a low-resolution graphics output, along with keyboard realtime reading, and a couple utilities enough to enable using a text terminal to run simple 2D games or simply rich terminal apps. The “noveau” factor is that it uses Unicode quarter-character block combinations to effectivelly enable 1/4 character “pixels” in the terminal. It also makes use of 24bit “true” color for text, not limiting itself to the 80’s 8 color palette […]

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Framework

A framework to build Slack apps using Python

bolt-python A Python framework to build Slack apps in a flash with the latest platform features. Read the getting started guide and look at our code examples to learn how to build apps using Bolt. Setup # Python 3.6+ required python -m venv .venv source .venv/bin/activate pip install -U pip pip install slack_bolt Creating an app Create a Bolt for Python app by calling a constructor, which is a top-level export. If you’d prefer, you can create an async app. […]

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Framework

A lightweight Python web API framework based on Flask

APIFlask APIFlask is a lightweight Python web API framework based on Flask and marshmallow-code projects. It’s easy to use, highly customizable, and 100% compatible with the Flask ecosystem. It starts as a fork of APIFairy and is inspired by FastAPI (see Comparison and Motivations for the comparison between these projects). More sugars for view function (@input(), @output(), @app.get(), @app.post() and more) Automatic request validation and deserialization (with Webargs) Automatic response formatting and serialization (with Marshmallow) Automatic OpenAPI Specification (OAS, formerly […]

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Framework

A dependency injection framework for Python

Dependency Injector Dependency Injector is a dependency injection framework for Python. It helps implementing the dependency injection principle. Key features of the Dependency Injector: Providers. Provides Factory, Singleton, Callable, Coroutine, Object, List, Dict, Configuration, Resource, Dependency and Selector providers that help assembling your objects. See Providers. Overriding. Can override any provider by another provider on the fly. This helps in testing and configuring dev / stage environment to replace API clients with stubs etc. See Provider overriding. Configuration. Reads configuration […]

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Python

Deploying a Python Flask Example Application Using Heroku

Welcome! Today we’re going to start building a Flask app that calculates word-frequency pairs based on the text from a given URL. This is a full-stack tutorial. Updates: 02/08/2020: Upgraded to Python version 3.8.1, and use python -m venv instead of pyvenv. 03/22/2016: Upgraded to Python version 3.5.1, and added autoenv version 1.0.0. 02/22/2015: Added Python 3 support. Part One: Set up a local development environment and then deploy both a staging and a production environment on Heroku. (current) Part […]

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