Hugging Face Supporter

Advanced-Deep-Learning-with-Keras

Advanced Deep Learning with Keras, published by Packt

datacards

Find Hugging face datasets that are missing tags. Then Help to fill then in; one-by-one

HFSpaces

Hugging Face Spaces

Machine-Learning-Using-TensorFlow-Cookbook

Machine Learning Using TensorFlow Cookbook, published by Packt

Multi-Label-Classification-of-Pubmed-Articles-Deployed-on-HuggingFace-Spaces

The traditional machine learning models give a lot of pain when we do not have sufficient labeled data for the specific task or domain we care about to train a reliable model. Transfer learning allows us to deal with these scenarios by leveraging the already existing labeled data of some related task or domain. We try to store this knowledge gained in solving the source task in the source domain and apply it to our problem of interest. In this work, I have utilized Transfer Learning utilizing BioBERT model. Also Applied RobertaForSequenceClassification and XLNetForSequenceClassification models for Fine-Tuning the Model. Model is live on Hugging Face Spaces

pytorch-book

Code included in the book, PyTorch Pocket Reference

pytorch-deep-learning

Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.

tftokenizers

Use Huggingface Transformer and Tokenizers as Tensorflow Reusable SavedModels

UDSMProt

Protein sequence classification with self-supervised pretraining

Yolo-V7-Custom-Dataset-Train-on-Kaggle

Hugging-Face-Supporter/HFSpaces

Hugging Face Spaces

Hugging-Face-Supporter/tftokenizers

Use Huggingface Transformer and Tokenizers as Tensorflow Reusable SavedModels

Hugging-Face-Supporter/Multi-Label-Classification-of-Pubmed-Articles-Deployed-on-HuggingFace-Spaces

The traditional machine learning models give a lot of pain when we do not have sufficient labeled data for the specific task or domain we care about to train a reliable model. Transfer learning allows us to deal with these scenarios by leveraging the already existing labeled data of some related task or domain. We try to store this knowledge gained in solving the source task in the source domain and apply it to our problem of interest. In this work, I have utilized Transfer Learning utilizing BioBERT model. Also Applied RobertaForSequenceClassification and XLNetForSequenceClassification models for Fine-Tuning the Model. Model is live on Hugging Face Spaces

Hugging-Face-Supporter/datacards

Find Hugging face datasets that are missing tags. Then Help to fill then in; one-by-one

Hugging-Face-Supporter/Advanced-Deep-Learning-with-Keras

Advanced Deep Learning with Keras, published by Packt

Hugging-Face-Supporter/Deep-Learning-with-TensorFlow-2-and-Keras

Deep Learning with TensorFlow 2 and Keras, published by Packt

Hugging-Face-Supporter/Diabetes-prediction

Hugging-Face-Supporter/fastbook

The fastai book, published as Jupyter Notebooks

Hugging-Face-Supporter/Flask-Calculator-basic

Hugging-Face-Supporter/Hands-on-Kubernetes-on-Azure-Third-Edition

Hugging-Face-Supporter/Machine-Learning-Using-TensorFlow-Cookbook

Machine Learning Using TensorFlow Cookbook, published by Packt

Hugging-Face-Supporter/pytorch-book

Code included in the book, PyTorch Pocket Reference

Hugging-Face-Supporter/pytorch-deep-learning

Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.

Hugging-Face-Supporter/UDSMProt

Protein sequence classification with self-supervised pretraining

Hugging-Face-Supporter/Yolo-V7-Custom-Dataset-Train-on-Kaggle

Hugging-Face-Supporter/Flask-app

Hugging-Face-Supporter/flask-ml-azure-serverless

This project builds a Continuous Integration pipeline using GitHub Actions, and a Continuous Delivery pipeline using Azure Pipelines for a Machine Learning Application.

Hugging-Face-Supporter/Hands-On-Computer-Vision-with-TensorFlow-2

Hands-On Computer Vision with TensorFlow 2, published by Packt

Hugging-Face-Supporter/handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

Hugging-Face-Supporter/handson-ml3

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

Hugging-Face-Supporter/lightning

Build and train PyTorch models and connect them to the ML lifecycle using Lightning App templates, without handling DIY infrastructure, cost management, scaling, and other headaches.

Hugging-Face-Supporter/notebooks

Jupyter notebooks for the Natural Language Processing with Transformers book

Hugging-Face-Supporter/pathml

Tools for computational pathology

Hugging-Face-Supporter/pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration