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ahmedfgad
Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
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Explore the skillahmedfgad
Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
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Explore the skillcoding-kitties
Framework for quantitative trading. Complete framework for development, backtesting, and deploying automated trading algorithms and trading bots.
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Explore the skillcafferychen777
Cell type annotation for single-cell RNA-seq using multi-LLM consensus
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Explore the skill[omh] Hermes Adversarial Consensus workflow: independent perspectives attack a proposal, then distill into a bundle a separate planner consumes. Use when the user says:…
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Explore the skillhyperledger
Hyperledger Fabric is an enterprise-grade permissioned distributed ledger framework for developing solutions and applications. Its modular and versatile design satisfies…
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Explore the skilltendermint
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Explore the skilltangyudi
人工智能学习路线图,整理近200个实战案例与项目,免费提供配套教材,零基础入门,就业实战!包括:Python,数学,机器学习,数据分析,深度学习,计算机视觉,自然语言处理,PyTorch tensorflow machine-learning,deep-learning data-analysis data-mining mathema…
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Explore the skillchangyeyu
🌟100+ 原创 LLM / RL 原理图📚,《大模型算法》作者巨献!💥(100+ LLM/RL Algorithm Maps )
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Explore the skillhora-search
🚀 efficient approximate nearest neighbor search algorithm collections library written in Rust 🦀 .
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Explore the skillhuoyijie
AdvancedEAST is an algorithm used for Scene image text detect, which is primarily based on EAST, and the significant improvement was also made, which make long text pred…
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Explore the skillhimself65
Prepare a pre-earnings briefing with consensus estimates, beat-miss history, and current market context.
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Explore the skillscikit-tda
Kepler Mapper: A flexible Python implementation of the Mapper algorithm.
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Explore the skillopen-spaced-repetition
A spaced repetition algorithm based on DSR model
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Explore the skilljavifalces
HFTFramework utilized for research on " A reinforcement learning approach to improve the performance of the Avellaneda-Stoikov market-making algorithm "