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João Carlos Néto's Projects

20-ml-algorithms-from-start-to-finish icon 20-ml-algorithms-from-start-to-finish

I want to solve a popular machine learning Data-set as a comprehensive workflow with python packages. After reading, you can use this workflow to solve other real problems and use it as a template to deal with machine learning problems.

a-comprehensive-ml-workflow-for-regression-methods icon a-comprehensive-ml-workflow-for-regression-methods

This is a comprehensive ML workflow for regression methods, I have tried to help Fans of Machine Learning with how to face machine learning regression problems. and I think it is a great opportunity for who want to learn machine learning workflow with python completely.

allennlp icon allennlp

An open-source NLP research library, built on PyTorch.

allennlp_tutorial icon allennlp_tutorial

Tutorial on how to use AllenNLP for sequence modeling (including hierarchical LSTMs and CRF decoding)

araml icon araml

Codes for our paper at EMNLP2019

asapppy icon asapppy

Semantic Textual Similarity and Dialogue System package for Python

audio_gpt icon audio_gpt

AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head

audiomentations icon audiomentations

A Python library for audio data augmentation. Inspired by albumentations. Useful for machine learning.

awesome-ai-ml-dl icon awesome-ai-ml-dl

Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics.

awesome-chatbot icon awesome-chatbot

Awesome Chatbot Projects,Corpus,Papers,Tutorials.Chinese Chatbot =>:

awesome-nlp icon awesome-nlp

:book: A curated list of resources dedicated to Natural Language Processing (NLP)

awesome-nlprojects icon awesome-nlprojects

List of projects related to Natural Language Processing (NLP) that make a geek smile for they exist

backprop icon backprop

Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.

bertopic icon bertopic

Leveraging BERT and c-TF-IDF to create easily interpretable topics.

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