ericmjl/dl-workshop
Crash course to master gradient-based machine learning. Also secretly a JAX course in disguise!
This workshop helps data scientists and machine learning engineers understand how gradient-based machine learning models work from the ground up. It takes you through the core concepts of deep learning, showing you how models learn without relying on existing frameworks. You'll gain a deeper intuition for how models, loss functions, and optimizers interact, which will help you better understand and troubleshoot complex deep learning systems.
233 stars. No commits in the last 6 months.
Use this if you are a data scientist or machine learning engineer who wants to build a foundational understanding of deep learning algorithms and gradient descent, independent of specific ML frameworks.
Not ideal if you are looking for a quick guide on how to use a specific deep learning framework like TensorFlow or PyTorch for a practical application.
Stars
233
Forks
52
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Feb 12, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ericmjl/dl-workshop"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
PaddlePaddle/Paddle
PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice...
fastai/fastai
The fastai deep learning library
openvinotoolkit/openvino_notebooks
📚 Jupyter notebook tutorials for OpenVINO™
PaddlePaddle/docs
Documentations for PaddlePaddle
msuzen/bristol
Parallel random matrix tools and complexity for deep learning