Deep Learning, NLP, NMT, AI, ML
I have used python functional programming to make this Ai assistant. we have seen in our daily life goggle assistant siri Alexa , I was pretty much interested to know how this things are working . So I had worked on this project to learn how they are working . I want to make more personalized ai assistant, so I had created this one. pyttsx3 (python text to speach): to convert text in voice ; datetime: for get the info […]
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Overview The goal of this collection is to provide an easier way to interact with Juniper’s Apstra solution. While nothing will stop you from using the built-in module, you may find that working with pre-packaged modules can help simplify the development of your playbook, or it may just be easier to support as a team. 📋 Ansible version compatibility There are significant changes to Ansible within version 3.x, and while those changes get worked out we will continue to test […]
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ONNX Runtime is a cross-platform inference and training machine-learning accelerator. ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. Learn more → ONNX Runtime training can accelerate the […]
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Build status ANTLR (ANother Tool for Language Recognition) is a powerful parser generator for reading, processing, executing, or translating structured text or binary files. It’s widely used to build languages, tools, and frameworks. From a grammar, ANTLR generates a parser that can build parse trees and also generates a listener interface (or visitor) that makes it easy to respond to the recognition of phrases of interest. Authors and major contributors
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Parallel WaveGAN implementation with Pytorch
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Clarity mode is a single-notebook interface built with existing JupyterLab components. To install: Clone this repository Ensure you have installed jupyter-server (pip install jupyter-server) Run pip install -e . npm install npm run build jupyter clarity In the URL, enter /clarity/path + the path to a notebook, e.g. localhost:8888/clarity/path/mynotebook.ipynb GitHub View Github
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Flashback is an awesome, retro IRC based app built using Django (and the Django Rest Framework) for the backend as well as React for the frontend! This project was made during the Summer Code Jam 2020. How to use Once you login or sign up to Flashback, you have access to the terminal! Here you run a few commands. You can connect to group chats, which allow you to contact chat with other Flashback users. You can also create your […]
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Dev – Alison Parker :: Telegram hack bot [ For Dev ] How to Install #———- —–Enjoy #Version 1.0 ::::: sudo -H pip install –upgrade youtube-dl sudo apt-get install -y libav-tools git clone https://github.com/red-alison/Hackbot.git cd Hackbot pip install lxml pip install telepot pip install urllib2 pip install requests pip install bs4 pip install wikipedia apt-get install youtube-dl -y pip install youtube-dl -U cd Hackbot echo APIKEY > api.txt python Hackbot.py GitHub – RED-ALISON/Hackbot at pythonawesome.com Telegram hack bot [ For […]
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YES, OMG YES. Check this out Python Default GC Settings: SQLAlchemy – 20,000 records in one query 1,859 GCs 908ms 78.7 MB mem MongoDB – 20,000 records in one query 463 GCs 593ms 75.8 MB mem Talk Python-optimized GC Settings: SQLAlchemy – 20,000 records in one query: 29 GCs (64x improvement) 695ms (23% improvement) 76.8 MB mem (surprisingly: 2% improvement with less GC) MongoDB – 20,000 records in one query 10 GCs (46x improvement) 515ms (13%) 72.3 MB mem (surprisingly: […]
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#Manage your employees! With all employee information stored in one place, you no longer have to sift through hoards of spreadsheets to manually search and enter data. Just enter data once, and use it across all web platform. App contain ability add employee and place where employee work – plant or salon Firstly, employee must be log in. Use email and password which contains hashing in database. Otherwise, information can not edit. If you successfully log in freely use application.
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