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Soft stacking! Soft stacking adalah teknik dalam machine learning yang digunakan untuk menggabungkan prediksi dari beberapa model yang berbeda untuk meningkatkan akurasi dan kinerja keseluruhan. Dalam soft stacking, prediksi dari setiap model digunakan sebagai input untuk model lain yang disebut "meta-model" atau "stacking model". Tujuan dari soft stacking adalah untuk: 1. *Meningkatkan akurasi*: Dengan menggabungkan prediksi dari beberapa model, soft stacking dapat meningkatkan akurasi keseluruhan. 2. *Mengurangi overfitting*: Soft stacking dapat membantu mengurangi overfitting dengan menggabungkan prediksi dari model yang berbeda. 3. *Meningkatkan robustez*: Soft stacking dapat membuat model lebih robust terhadap perubahan data atau kondisi yang berbeda. Soft stacking sering digunakan dalam kompetisi machine learning dan telah terbukti efektif dalam meningkatkan kinerja model. Apakah Anda ingin tahu lebih banyak tentang soft stacking atau teknik machine learning lainnya? #SoftStalking

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Soft stacking! Soft stacking adalah teknik dalam machine learning yang digunakan untuk menggabungkan prediksi dari beberapa model yang berbeda untuk meningkatkan akurasi dan kinerja keseluruhan. Dalam soft stacking, prediksi dari setiap model digunakan sebagai input untuk model lain yang disebut "meta-model" atau "stacking model". Tujuan dari soft stacking adalah untuk: 1. *Meningkatkan akurasi*: Dengan menggabungkan prediksi dari beberapa model, soft stacking dapat meningkatkan akurasi keseluruhan. 2. *Mengurangi overfitting*: Soft stacking dapat membantu mengurangi overfitting dengan menggabungkan prediksi dari model yang berbeda. 3. *Meningkatkan robustez*: Soft stacking dapat membuat model lebih robust terhadap perubahan data atau kondisi yang berbeda. Soft stacking sering digunakan dalam kompetisi machine learning dan telah terbukti efektif dalam meningkatkan kinerja model. Apakah Anda ingin tahu lebih banyak tentang soft stacking atau teknik machine learning lainnya?

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[26/07, 12:09 am] Meta AI: Chainbase is a decentralized data infrastructure platform designed for Web3 applications, providing a unified solution for blockchain data access and processing. It aggregates data from over 200 blockchains, including Ethereum, Bitcoin, and Solana, and delivers it through a user-friendly dashboard and API interface. *Key Features:* - *Unified Data Access*: Chainbase supports multiple blockchains, enabling developers to access and interact with blockchain data seamlessly. - *Real-Time Data Streams*: The platform provides live data pipelines for instant tracking of transactions, asset prices, [26/07, 12:09 am] Meta AI: Chainbase is a decentralized data infrastructure platform designed for Web3 applications, providing a unified solution for blockchain data access and processing. It aggregates data from over 200 blockchains, including Ethereum, Bitcoin, and Solana, and delivers it through a user-friendly dashboard and API interface. *Key Features:* - *Unified Data Access*: Chainbase supports multiple blockchains, enabling developers to access and interact with blockchain data seamlessly. - *Real-Time Data Streams*: The platform provides live data pipelines for instant tracking of transactions, asset prices, wallet balances, and market trends. - *AI-Powered Insights*: Chainbase integrates advanced AI features like Theia, allowing users to query blockchain data using natural language. - *Decentralized Data Infrastructure*: Chainbase ensures data integrity and availability across multiple nodes, providing a high level of resilience and security for Web3 applications. @ChainbaseHQ #chainbase

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Lagrange is a blockchain infrastructure project that enables efficient, scalable, and secure computation through zero-knowledge proofs (ZK proofs). It combines a decentralized ZK Prover Network with a hyper-parallel ZK Coprocessor, allowing for advanced verifiable computation use cases like AI inference, cross-chain messaging, and rollup verification. *Key Features:* - *Decentralized Proof Production*: Lagrange's network of provers generates ZK proofs on demand, ensuring high availability and censorship resistance. - *Modular Architecture*: Independent subnets support different computational needs, providing customized proof resources for blockchains, rollups, or applications without bottlenecks. - *DARA Mechanism*: Double Auction Resource Allocation optimizes resource allocation, ensuring fair pricing and minimizing system waste. *Use Cases:* - *Verifiable AI*: zkML technology enables cryptographic verification of AI inferences, ensuring transparency and security. - *Cross-Chain Interoperability*: Seamless connections between blockchains and applications promote development of the cross-chain ecosystem. - *Rollup Infrastructure*: Lagrange supports scalable verification for Layer 2 solutions. *Token Information:* - *Token Name*: Lagrange (LA) - *Total Supply*: 1 billion tokens - *Token Utility*: Payment for proof generation, staking/delegation, and DAO governance #lagrange #LA

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