🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
- python
- machine-learning
- explainability
- explainable-ml
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🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Massively parallel rigidbody physics simulation on accelerator hardware.
📚 Jupyter notebook tutorials for OpenVINO™
Official Repository for "Eureka: Human-Level Reward Design via Coding Large Language Models" (ICLR 2024)
Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) by way of Textual Inversion (https://arxiv.org/abs/2208.01618) for Stable Diffusion (https://arxiv.org/abs/2112.10752). Tweaks focused on training faces, objects, and styles.
Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023
The repository host gives no description.
深度学习100例、深度学习DL、图片分类、目标识别、目标检测、自然语言处理nlp、文本分类、TensorFlow、PyTorch
Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL
The repository is about 100+ python programming exercise problem discussed, explained, and solved in different ways
A python library for decision tree visualization and model interpretation.
收集Android方方面面的经典知识, 最新技术.
A high performance implementation of HDBSCAN clustering.
NMA Computational Neuroscience course
An open-source project dedicated to tracking and segmenting any objects in videos, either automatically or interactively. The primary algorithms utilized include the Segment Anything Model (SAM) for key-frame segmentation and Associating Objects with Transformers (AOT) for efficient tracking and propagation purposes.
小型金融知识图谱构建流程(neo4j / python / cypher / KG)
Learn to build your Second Brain AI assistant with LLMs, agents, RAG, fine-tuning, LLMOps and AI systems techniques.
TradeMaster is an open-source platform for quantitative trading empowered by reinforcement learning :fire: :zap: :rainbow:
100 data puzzles for pandas, ranging from short and simple to super tricky (60% complete)
Runs 405B LLMs on 8GB VRAM
Productivity Tools for Plotly + Pandas
LLMs-from-scratch项目中文翻译
Optimized Stable Diffusion modified to run on lower GPU VRAM
Quantitative analysis, strategies and backtests