Hugging Face Supporter's repositories
tftokenizers
Use Huggingface Transformer and Tokenizers as Tensorflow Reusable SavedModels
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
Advanced-Deep-Learning-with-Keras
Advanced Deep Learning with Keras, published by Packt
Deep-Learning-with-TensorFlow-2-and-Keras
Deep Learning with TensorFlow 2 and Keras, published by Packt
fastbook
The fastai book, published as Jupyter Notebooks
Machine-Learning-Using-TensorFlow-Cookbook
Machine Learning Using TensorFlow Cookbook, published by Packt
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.
UDSMProt
Protein sequence classification with self-supervised pretraining
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.
Hands-On-Computer-Vision-with-TensorFlow-2
Hands-On Computer Vision with TensorFlow 2, published by Packt
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.
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.
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.
notebooks
Jupyter notebooks for the Natural Language Processing with Transformers book
pathml
Tools for computational pathology
pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration