dataflowr/notebooks
code for deep learning courses
This project provides practical, hands-on examples for learning deep learning concepts and techniques using PyTorch. It takes you from foundational elements like tensors and automatic differentiation to advanced topics such as transformers and diffusion models. The materials are designed for students and practitioners who want to understand and implement deep learning models for various tasks.
1,259 stars.
Use this if you are a student or practitioner looking for structured, code-based examples to learn and apply deep learning.
Not ideal if you are a non-technical user seeking a no-code solution or a seasoned deep learning engineer looking for advanced research implementations.
Stars
1,259
Forks
331
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Feb 10, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/dataflowr/notebooks"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
mrdbourke/pytorch-deep-learning
Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
xl0/lovely-tensors
Tensors, for human consumption
stared/livelossplot
Live training loss plot in Jupyter Notebook for Keras, PyTorch and others
dvgodoy/PyTorchStepByStep
Official repository of my book: "Deep Learning with PyTorch Step-by-Step: A Beginner's Guide"
jeffheaton/app_deep_learning
T81-558: PyTorch - Applications of Deep Neural Networks @Washington University in St. Louis