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Machine Learning Basics
A practical set of notebooks on machine learning basics, implemented in both TF2.0 + Keras and PyTorch.
deep-learning natural-language-processing tensorflow pytorch
ML in Production - Deployment Series
A multi-part blog series on deploying machine learning models in an automated, reproducible, and auditable manner.
production guide article tutorial
Full Stack Deep Learning
Full Stack Deep Learning helps you bridge the gap from training machine learning models to deploying AI systems in the real world.
production full-stack deep-learning course
Top Down Introduction to BERT with HuggingFace and PyTorch
I will also provide some intuition into how BERT works with a top down approach (applications to algorithm).
bert top-down huggingface pytorch
Putting ML in Production
A guide and case study on MLOps for software engineers, data scientists and product managers.
production mlops course tutorial
Deep Tutorials for PyTorch
This is a series of in-depth tutorials I'm writing for implementing cool deep learning models on your own with the amazing PyTorch library.
image-captioning sequence-labeling object-detection text-classification
MLOps Tutorial Series
How to create an automatic model training & testing setup using GitHub Actions and Continuous Machine Learning (CML).
ci-cd ml-ops production github-actions
PyTorch Transformers Tutorials
A set of annotated Jupyter notebooks, that give user a template to fine-tune transformers model to downstream NLP tasks such as classification, NER etc.
transformers text-classification text-summarization named-entity-recognition
DETR: End-to-End Object Detection with Transformers
A new method that views object detection as a direct set prediction problem.
object-detection panoptic-segmentation transformers computer-vision
The Future of (Transfer Learning in) Natural Language Processing
Transfer Learning in Natural Language Processing (NLP): Open questions, current trends, limits, and future directions.
natural-language-processing transfer-learning tutorial video
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