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
Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications. Tracing · Evals · Simulations · Datasets · Gateway · Guardrails. Self-hostable. Apache 2.0.
Machine Learning notebooks for refreshing concepts.
Local Interpretable Model-Agnostic Explanations (R port of original Python package)
🔍 Minimal examples of machine learning tests for implementation, behaviour, and performance.
an MLOps/LLMOps platform
AI-powered NBA game outcome predictor that uses advanced team stats and trend-based features to forecast winners and track model performance
CloudCV GSoC Ideas
UBC ARBERT and MARBERT Deep Bidirectional Transformers for Arabic
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.
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
Community registry of LLM serving-path traps that produce confidently wrong measurements: templates, tool parsers, reasoning fields, quant kernel paths, CUDA toolchains, KV allocation, eval harnesses, versioning. Symptom-first, with the check that catches each.
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
Ollama Model Test - Figure out the best model for the task
OpenLLM Monitor is a plug-and-play, real-time observability dashboard for monitoring and debugging LLM API calls across OpenAI, Ollama, OpenRouter, and more. Tracks tokens, latency, cost, retries, and lets you replay prompts — fully open-source and self-hostable.
Valor is a lightweight, numpy-based library designed for fast and seamless evaluation of machine learning models.
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