SkalskiP/ILearnDeepLearning.py
This repository contains small projects related to Neural Networks and Deep Learning in general. Subjects are closely linekd with articles I publish on Medium. I encourage you both to read as well as to check how the code works in the action.
This project provides practical code examples and visualizations to help you understand how neural networks work. It takes complex theoretical concepts, like gradient descent and overfitting, and illustrates them with clear animations. Data scientists, machine learning practitioners, or students learning deep learning would use this to see abstract ideas in action.
1,400 stars. No commits in the last 6 months.
Use this if you are a data scientist or machine learning practitioner looking for concrete code examples and visualizations to deepen your understanding of neural network fundamentals and common challenges.
Not ideal if you are looking for a ready-to-use library or a production-ready application; this project focuses on educational examples rather than deployable solutions.
Stars
1,400
Forks
465
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Dec 10, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/SkalskiP/ILearnDeepLearning.py"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
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