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IIIT, Bangalore
- Bangalore
- https://www.youtube.com/channel/UCoz8NrwgL7U9535VNc0mRPA/
- @rattller
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Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
This repository, called fast sentence transformers, contains code to run 5X faster sentence transformers using tools like quantization and ONNX.
FastAPI framework, high performance, easy to learn, fast to code, ready for production
Spriteworld: a flexible, configurable python-based reinforcement learning environment
Natural Language Processing Tutorial for Deep Learning Researchers
Resources and tools for Indian language Natural Language Processing
State-of-the-Art Language Modeling and Text Classification in Hindi Language
Code for Document Similarity on Reuters dataset using Encode, Embed, Attend, Predict recipe
FORMCEPT Whiteboard used by developers for developers
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
Keras DL models to answer 8th grade science multiple choice questions (Kaggle AllenAI competition).
Clustering a set of word/tags using K-Means with word2vec or wordnet distance
📖 Some language modeling tools for Keras
A curated list of awesome Go frameworks, libraries and software
Introduction to Deep Learning for Natural Language Processing
Set of Machine Learning and Stochastic Optimazion tools based on Hadoop, Spark and Storm https://pkghosh.wordpress.com/
Distributed Deep learning with Keras & Spark
Orientdb driver for python that uses the binary protocol.
"Forensic" in this case is just a fancy word for "file parsers". Sounds a hell of a lot better, you know.
An Open Source Machine Learning Framework for Everyone
Models and examples built with TensorFlow
elbamos / Zeppelin-With-R
Forked from apache/zeppelinMirror of Apache Zeppelin (Incubating)
Apache Spark - A unified analytics engine for large-scale data processing
💬 A community-driven python bot that aims to be as simple as possible to serve humans with their everyday tasks