RadIO is a library for data science research of computed tomography imaging

Temporal Causal Discovery Framework (PyTorch): discovering causal relationships between time series

Unsupervised Scalable Representation Learning for Multivariate Time Series: Experiments

links to conference publications in graph-based deep learning

These are my notes which I prepared during deep learning specialization taught by AI guru Andrew NG. I have used diagrams and code snippets from the code whenever needed but following The Honor Code.

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models

AI-related tutorials. Access any of them for free → https://towardsai.net/editorial

A blog which talks about machine learning, deep learning algorithms and the Math. and Machine learning algorithms written from scratch.

Applying Data Science and Machine Learning to Solve Real World Business Problems

Machine Learning notebooks for refreshing concepts.

My personal notes, presentations, and notebooks on everything Deep Learning.

🧑‍🏫 50! Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

Anomaly detection for temporal data using LSTMs

Deep learning codes and projects using Python

All about attention in neural networks. Soft attention, attention maps, local and global attention and multi-head attention.

A clear, concise, simple yet powerful and efficient API for deep learning.

Amacımız Türkçe NLP literatüründeki birçok farklı sorunu bir arada çözebilen, eşsiz yaklaşımlar öne süren ve literatürdeki çalışmaların eksiklerini gideren open source bir yazım destekleyicisi/denetleyicisi oluşturmak. Kullanıcıların yazdıkları metinlerdeki yazım yanlışlarını derin öğrenme yaklaşımıyla çözüp aynı zamanda metinlerde anlamsal analizi de gerçekleştirerek bu bağlamda ortaya çıkan yanlışları da fark edip düzeltebilmek.

🟣 Object detection of abstract shapes with neural networks

Tutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery. This model can be used to identify newly developed or flooded land. Uses ground-truth labels and processed NAIP imagery provided by the Chesapeake Conservancy.

Ever wondered how to code your Neural Network using NumPy, with no frameworks involved?