General Assembly's 2015 Data Science course in Washington, DC
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Updated
Jun 5, 2024 - Jupyter Notebook
General Assembly's 2015 Data Science course in Washington, DC
Local Interpretable Model-Agnostic Explanations (R port of original Python package)
Machine Learning notebooks for refreshing concepts.
🔍 Minimal examples of machine learning tests for implementation, behaviour, and performance.
an MLOps/LLMOps platform
CloudCV GSoC Ideas
UBC ARBERT and MARBERT Deep Bidirectional Transformers for Arabic
Measure and visualize machine learning model performance without the usual boilerplate.
Flexible tool for bias detection, visualization, and mitigation
A High-level Scorecard Modeling API | 评分卡建模尽在于此
An in-depth analysis of audio classification on the RAVDESS dataset. Feature engineering, hyperparameter optimization, model evaluation, and cross-validation with a variety of ML techniques and MLP
Customers in the telecom industry can choose from a variety of service providers and actively switch from one to the next. With the help of ML classification algorithms, we are going to predict the Churn.
This project analyzes and visualizes the Used Car Prices from the Automobile dataset in order to predict the most probable car price
An Interactive Approach to Understanding Deep Learning with Keras
Valor is a centralized evaluation store which makes it easy to measure, explore, and rank model performance.
🎓 2020 Undergraduate Graduation Project in Jiangnan University ALL codes including Data-convert, keras-Train, model-Evaluate and Web-App
Use AutoAI to detect fraud
Evaluate the performance of computer vision models and prompts for zero-shot models (Grounding DINO, CLIP, BLIP, DINOv2, ImageBind, models hosted on Roboflow)
Rapid Calculation of Model Metrics
Titus 2 : Portable Format for Analytics (PFA) implementation for Python 3.4+
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