Hessian Collection

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⚡Thunder

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“SOFARPC is a high-performance, high-extensibility, production-level Java RPC framework.”

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🔮Psychic

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“PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).”

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🔥Fire

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“Robotics Toolbox for Python”

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⚡Thunder

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“Jupiter是一款性能非常不错的, 轻量级的分布式服务框架”

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📦Normal

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“PyHessian is a Pytorch library for second-order based analysis and training of Neural Networks”

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⚡Thunder

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“A RPC service development framework.（RPC服务框架XXL-RPC）”

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📦Normal

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“A Tutorial on Manipulator Differential Kinematics”

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🔮Psychic

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“Quantization library for PyTorch. Support low-precision and mixed-precision quantization, with hardware implementation through TVM.”

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🔮Psychic

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“ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning”

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🔮Psychic

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“Code and weights for local feature affine shape estimation paper "Repeatability Is Not Enough: Learning Discriminative Affine Regions via Discriminability"”

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🌊Water

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“caucho hessian2 implementation in Go for [apache/dubbo-go](https://github.com/apache/dubbo-go) which is compatible with [dubbo-hessian-lite](https://github.com/apache/dubbo-hessian-lite)”

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⚡Thunder

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“A lightweight, high throughput, and low latency RPC framework that supports the RDMA protocol”

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🔮Psychic

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“{KFAC,EKFAC,Diagonal,Implicit} Fisher Matrices and finite width NTKs in PyTorch”

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⚡Thunder

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“⚡️ Nepxion Thunder is a distribution RPC framework based on Netty + Hessian + Kafka + ActiveMQ + Tibco + Zookeeper + Redis + Spring Web MVC + Spring Boot + Docker 多协议、多组件、多序列化的分布式RPC调用框架”

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⚡Electric

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“js hessian binary web service protocol, support communicate with java”

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⚡Thunder

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“A high-performance, open-source java RPC framework.”

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⚡Thunder

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“An internal improved version of Hessian3/4 powered by Ant Group CO., Ltd.”

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📦Normal

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“This Repository contains Solutions to the Quizes & Lab Assignments of the Mathematics for Machine Learning Specialization offered by Imperial College of London on Coursera taught by David Dye, Samuel J. Cooper, A. Freddie Page, Marc Peter Deisenroth”