[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9856":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":16,"forks30d":16,"starsTrendScore":17,"compositeScore":19,"rankGlobal":10,"rankLanguage":10,"license":10,"archived":20,"fork":20,"defaultBranch":21,"hasWiki":22,"hasPages":20,"topics":23,"createdAt":10,"pushedAt":10,"updatedAt":32,"readmeContent":33,"aiSummary":34,"trendingCount":16,"starSnapshotCount":16,"syncStatus":17,"lastSyncTime":35,"discoverSource":36},9856,"Deep-Learning-with-PyTorch-Tutorials","dragen1860\u002FDeep-Learning-with-PyTorch-Tutorials","dragen1860","深度学习与PyTorch入门实战视频教程 配套源代码和PPT","https:\u002F\u002Fstudy.163.com\u002Fcourse\u002Fintroduction\u002F1208894818.htm",null,"Python",3114,1317,68,5,0,2,6,59.96,false,"master",true,[24,25,26,27,28,29,30,31],"artificial-intelligence","convolutional-neural-networks","deep-learning","generative-adversarial-network","machine-learning","pytorch","recurrent-neural-networks","tutorial","2026-06-12 04:00:47","# PyTorch安装指令\n请先安装Anaconda和CUDA 10.0。\n\n- 配置国内源\n\n```python\n# 配置国内源，方便安装Numpy,Matplotlib等\nconda config --add channels https:\u002F\u002Fmirrors.tuna.tsinghua.edu.cn\u002Fanaconda\u002Fpkgs\u002Ffree\u002F\nconda config --add channels https:\u002F\u002Fmirrors.tuna.tsinghua.edu.cn\u002Fanaconda\u002Fpkgs\u002Fmain\u002F\n# 配置国内源，安装PyTorch用\nconda config --add channels https:\u002F\u002Fmirrors.tuna.tsinghua.edu.cn\u002Fanaconda\u002Fcloud\u002Fpytorch\u002F\n# 显示源地址\nconda config --set show_channel_urls yes\n```\n\n- 安装PyTorch\n```python\n# 安装PyTorch，要使用国内源请去掉-c pytorch这个参数！！\nconda install pytorch torchvision cudatoolkit=10.0\n\n```\n\n- 安装常用库\n\n```python\npip install numpy matplotlib pillow pandas\n```\n\n# 课程链接\n\n\u003C!--  \n\u003Cp align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fstudy.163.com\u002Fcourse\u002FcourseMain.htm?share=2&shareId=480000001847407&courseId=1208894818&_trace_c_p_k2_=61a9e0a511f7409b92a08d4f4c964330\n\">\n    \u003Cimg src=\"res\u002Fad_banner.png\">\n  \u003C\u002Fa>\n\u003C\u002Fp> \n -->\n**课程链接:** https:\u002F\u002Fstudy.163.com\u002Fcourse\u002FcourseMain.htm?share=2&shareId=480000001847407&courseId=1208894818&_trace_c_p_k2_=61a9e0a511f7409b92a08d4f4c964330\n\n\u003Cp align=\"center\">\n  \u003Cimg width=\"700\"  src=\"res\u002F版权声明.png\">\n\u003C\u002Fp> \n\n \n**课程大纲:**\n![课程介绍](res\u002Foutline.png)\n\n\n\n\n\n","该项目提供了一套深度学习与PyTorch入门实战的视频教程，附带源代码和PPT。其核心功能在于通过一系列精心设计的教学内容帮助初学者理解并掌握使用PyTorch进行深度学习模型构建的方法，涵盖了从环境配置到复杂神经网络实现的全过程。技术特点包括对卷积神经网络、循环神经网络及生成对抗网络等当前热门领域的详细介绍。非常适合希望快速入门深度学习领域，并且偏好动手实践的学习者或开发者使用，在学术研究和个人项目开发等多个场景下均可发挥作用。","2026-06-11 03:25:02","top_topic"]