In the past decade, thanks to the powerfulness of deep-learning techniques, we have witnessed a whole new era of automated code generation.To sort out developments, we have conducted a comprehensive review of solutions to deep learning-based code generation.In this survey, we generally formalize...
You can use GPU Coder™ in tandem with the Deep Learning Toolbox™ to generate code and deploy CNN on multiple embedded platforms that use NVIDIA®or ARM®GPU processors. The Deep Learning Toolbox provides simple MATLAB®commands for creating and interconnecting the layers of a deep neur...
You can useMATLAB®Coder™with Deep Learning Toolbox to generate C++ code from a trained deep learning network. You can then deploy the generated code to an embedded platform that uses an Intel®or ARM®processor. You can also generate generic C or C++ code from a trained deep learnin...
Small network that can learn to solve CartPole environment with reinforcement learning https://gym.openai.com/envs/CartPole-v0/ Code is supposed to be clear and generalizable. Not the most optimized reinforcement learning for this problem.
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Create a code generation configuration object for a library or executable by usingcoder.config. Set theTargetLangproperty to'C++'. cfg = coder.config('exe'); cfg.TargetLang ='C++'; Create a deep learning configuration object by usingcoder.DeepLearningConfig. Set theArmComputeVersionandArm...
Use of the coder.loadDeepLearningNetwork function to load an resnet50 series network and generate CUDA® code for this network. Create an entry-point function resnetFun that uses the coder.loadDeepLearningNetwork function to call the Deep Learning Toolbox™ toolbox function resnet50. This ...
Therefore, this paper suggested, an innovative, unsupervised Deep learning assisted reconstructed coder (UDR-RC) which optimize the data during pre-processing at on-nodule wearable sensors to get minimized computation time of 11.25ns for test set size and improves recognition performance in the ...
1. 开发者：DeepSeek Coder 可以作为一个实用的代码助手，帮助开发者快速生成代码、修改代码、编写测试...
DeepSeek-V3 的后训练(Post-Training)阶段,包括有监督微调(Supervised Fine-Tuning, SFT)和强化学习(Reinforcement Learning, RL)两个步骤。 有监督微调 (SFT) SFT 阶段,DeepSeek-V3 在一个包含 1.5M 指令-响应对的高质量数据集上进行了微调。该数据集涵盖了多种任务类型和领域,并采用了不同的数据构建策略,以...