7868 Open source projects that are alternatives of or similar to Coursera Natural Language Processing Specialization

NLP in Python with Deep Learning

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👄 The most accurate natural language detection library for Java and the JVM, suitable for long and short text alike

Collection of useful data science topics along with code and articles

A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2021 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques!

Gorgonia is a library that helps facilitate machine learning in Go.

My implementation of the original transformer model (Vaswani et al.). I've additionally included the playground.py file for visualizing otherwise seemingly hard concepts. Currently included IWSLT pretrained models.

由淺入深的深度學習資源 Collection of deep learning materials for everyone

CogComp's Natural Language Processing libraries and Demos:

The hands-on NLTK tutorial for NLP in Python

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Go to https://github.com/pytorch/tutorials - this repo is deprecated and no longer maintained

Keras, PyTorch, and NumPy Implementations of Deep Learning Architectures for NLP

Code For Medium Article: "How To Create Natural Language Semantic Search for Arbitrary Objects With Deep Learning"

💫 Industrial-strength Natural Language Processing (NLP) in Python

PyTorch Tutorial (1.7)

This is the implementation of paper <Additive Margin Softmax for Face Verification>

Efficient Learning of Augmentation Policy Schedules

Machine learning based text classification in JavaScript using n-grams and cosine similarity

💥 Fast State-of-the-Art Tokenizers optimized for Research and Production

🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.

This repository contains my full work and notes on Coursera's NLP Specialization (Natural Language Processing) taught by the instructor Younes Bensouda Mourri and Łukasz Kaiser offered by deeplearning.ai

Lab Materials for MIT 6.S191: Introduction to Deep Learning

Deep Learning Specialization by Andrew Ng on Coursera.

Course repo for Applied Natural Language Processing (Spring 2019)

Simple XLNet implementation with Pytorch Wrapper

Monk is a low code Deep Learning tool and a unified wrapper for Computer Vision.

Medical Q&A with Deep Language Models

⛔ [NOT MAINTAINED] An End-To-End Closed Domain Question Answering System.

DeepLearning.ai课程学习Jupyter Notebook作业

基于自然语言理解与机器学习的聊天机器人，支持多用户并发及自定义多轮对话

深度学习入门教程, 优秀文章, Deep Learning Tutorial

💫 Automated codification of Greek Legislation with NLP

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The Python Code Tutorials

BERT score for text generation

An open source library for deep learning end-to-end dialog systems and chatbots.

The implementation of DeBERTa

coursera吴恩达机器学习课程作业自写Python版本+Matlab原版

The implementation of Relativistic average GAN with Keras

《机器学习宝典》包含：谷歌机器学习速成课程（招式）+机器学习术语表（口诀）+机器学习规则（心得）+机器学习中的常识性问题 （内功）。该资源适用于机器学习、深度学习研究人员和爱好者参考！

Notes for the Reinforcement Learning course by David Silver along with implementation of various algorithms.

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This Word Does Not Exist

This repo contains implementation of different architectures for emotion recognition in conversations.

国内公司人工智能方向（含机器学习、深度学习、计算机视觉和自然语言处理）岗位的招聘信息（含全职、实习和校招）

Sequence to sequence learning using TensorFlow.

A simplified PyTorch implementation of "SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient." (Yu, Lantao, et al.)

Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)

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Re-implementation of TensorFlow in pure python, with an emphasis on code understandability

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TensorFlow 2.x version's Tutorials and Examples, including CNN, RNN, GAN, Auto-Encoders, FasterRCNN, GPT, BERT examples, etc. TF 2.0版入门实例代码，实战教程。

Visualize and explore NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2).