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Deep learning is changing our lives in small and large ways every day. Whether it’s Siri or Alexa following our voice commands, the real-time translation apps on our phones, or the computer vision ...

Not every regression or classification problem needs to be solved with deep learning. For that matter, not every regression or classification problem needs to be solved with machine learning. After ...

中国深度学习市场应用规模第一！ 这就是中国信通院与深度学习技术及应用国家工程研究中心联合发布的《深度学习平台发展报告（2022年）》（下文简称报告）中，所给出的最新结论。 而且还是和老牌深度学习框架选手，谷歌家的TensorFlow、Meta家的PyTorch一较高 ...

自 2015 年 TensorFlow 开源以来，伴随着深度学习的迅猛发展，通用深度学习框架经历了 10 年的高速发展，大浪淘沙，余者寥寥。曾几何时，也有过性能与易用性之争，也有过学术界和工业界之分，但随着本轮大模型应用的推波助澜，PyTorch 无疑已经成为事实上的大 ...

In the dynamic world of machine learning, two heavyweight frameworks often dominate the conversation: PyTorch and TensorFlow. These frameworks are more than just a means to create sophisticated ...

Is PyTorch better than TensorFlow for general use cases? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world.

Pytorch是目前常用的深度学习框架之一，它凭借着对初学者的友好性、灵活性，发展迅猛，它深受学生党的喜爱，我本人也是使用的Pytorch框架。 比起 TF 的框架环境配置不兼容，和 Keras 由于高度封装造成的不灵活，PyTorch 无论是在学术圈还是工业界，都相当占优势。

深夜的实验室灯光下，代码在终端持续编译，TensorFlow日志飞速滚动，Jupyter Notebook里刚跑完第三轮超参数调优——AI研究人员需要的不是纸面参数堆砌的旗舰，而是真实场景中稳如磐石的算力基座：既要应对PyTorch分布式训练的线程调度压力，也要兼顾数据清洗 ...

深夜调试完Transformer结构，模型在验证集上刚跳出理想的loss曲线——此时你正需要一块不拖慢迭代节奏、不烧穿笔记本散热模组、又能稳跑LoRA微调与本地知识库RAG服务的显卡。对AI开发者与一线研究人员而言，显卡早已不止于渲染帧率，而是数据预处理的吞吐 ...