A scientific benchmark and comparison of the performance of sentiment analysis models in NLP on small to medium datasets

Some AI notebooks.

A performance benchmark of recent image classification models in deep learning

Benchmark of Multiple Imputation using Chained Equations (MICE) algorithms on missing value imputation

Benchmarking notebooks for various Persian G2P models, comparing their performance on the SentenceBench dataset, including Homo-GE2PE and Homo-T5.

Implementation, analysis and benchmarking of optimization algorithms. Developed in Python and results showed in Jupyter Notebook

Benchmarking for Jespipe to see if performance is better than Jupyter Notebook.

H.E.I.M.D.A.L.L looks at fleet telemetry and gives you natural-language insights. GPU data loading (cuDF), local LLM inference (Gemma 2), and production NIM on GKE. Open the notebooks, run cells, get answers! Quick start should not take longer than 10 minutes and the T4 path is completely free!

CX_BENCH - light and fast CPU benchmark. SIMPLE NOTE v1.1 - simple notebook. X-Arkanoid v1.0 - fun and easy 2D game. WEEK_TIMER and more...

A notebook benchmarking a recently developed Dimensionality Reduction technique using Siamese Networks supporting both supervised and unsupervised modes.

Collection of TensorFlow/Keras Jupyter notebooks demonstrating low-level APIs, custom training loops, callbacks, subclassed models, custom loss functions, transfer learning, and advanced deep learning architectures.

Analyze Twitter sentiment by leveraging MiniLM embeddings and compare machine learning models for accurate sentiment classification and visualization.

Benchmark Microsoft Foundry Content Understanding on CUAD legal contracts. Achieves 83.3% F1 score (29% better than GPT-4o baseline). Complete Python notebook with optimized schemas for contract clause extraction. Production-ready with confidence scores & cost analysis.

Jupyter notebooks on custom loss functions in TensorFlow/Keras: modified MSE penalizing overconfidence and Categorical Focal Loss with L1/L2 regularization for imbalanced multi-class tasks (e.g., cats_vs_dogs). Includes model building, preprocessing, GPU checks, and focuses on learning mechanics over metrics.