[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9770":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":16,"stars30d":17,"stars90d":16,"forks30d":16,"starsTrendScore":16,"compositeScore":18,"rankGlobal":10,"rankLanguage":10,"license":10,"archived":19,"fork":19,"defaultBranch":20,"hasWiki":21,"hasPages":19,"topics":22,"createdAt":10,"pushedAt":10,"updatedAt":33,"readmeContent":34,"aiSummary":35,"trendingCount":16,"starSnapshotCount":16,"syncStatus":17,"lastSyncTime":36,"discoverSource":37},9770,"TensorFlow-2.x-Tutorials","dragen1860\u002FTensorFlow-2.x-Tutorials","dragen1860","TensorFlow 2.x version's  Tutorials and Examples, including CNN, RNN, GAN, Auto-Encoders, FasterRCNN, GPT, BERT examples, etc. TF 2.0版入门实例代码，实战教程。","",null,"Jupyter Notebook",6352,2192,219,18,0,2,41,false,"master",true,[23,24,25,26,27,28,29,30,31,32],"artificial-intelligence","computer-vision","deep-learning","machine-learning","neural-network","nlp","tensorflow","tensorflow-2","tensorflow-examples","tensorflow-tutorials","2026-06-12 02:02:12","# TensorFlow 2.0 Tutorials \nOur repo. is the **Winner** of [⚡#PoweredByTF 2.0 Challenge!](https:\u002F\u002Fdevpost.com\u002Fsoftware\u002Ftensorflow-2-0-tutorials).\n\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"res\u002Ftensorflow-2.0.gif\" width=\"250\" align=\"middle\">\n\u003C\u002Fp>\n\nTimeline:\n- Oct. 1, 2019: TensorFlow 2.0 Stable!\n- Aug. 24, 2019: [TensorFlow 2.0 rc0](https:\u002F\u002Fwww.tensorflow.org\u002Fversions\u002Fr2.0\u002Fapi_docs\u002Fpython\u002Ftf)\n- Jun. 8, 2019: [TensorFlow 2.0 Beta](https:\u002F\u002Ftwitter.com\u002Ffchollet\u002Fstatus\u002F1134583289384120320)\n- Mar. 7, 2019: [Tensorflow 2.0 Alpha](https:\u002F\u002Fwww.tensorflow.org\u002Falpha)\n- Jan. 11, 2019: [TensorFlow r2.0 preview](https:\u002F\u002Fwww.tensorflow.org\u002Fversions\u002Fr2.0\u002Fapi_docs\u002Fpython\u002Ftf)\n- Aug. 14, 2018: [TensorFlow 2.0 is coming](https:\u002F\u002Fgroups.google.com\u002Fa\u002Ftensorflow.org\u002Fforum\u002F#!topic\u002Fdiscuss\u002Fbgug1G6a89A)\n\n\n\n# Installation\n\nmake sure you are using python 3.x.\n\n- CPU install\n```python\npip install tensorflow -U\n```\n\n- GPU install\n\nInstall `CUDA 10.0`(or after) and `cudnn` by yourself. and set `LD_LIBRARY_PATH` up.\n\n```python\npip install tensorflow-gpu  -U\n```\n\nTest installation:\n```python\nIn [2]: import tensorflow  as tf\n\nIn [3]: tf.__version__\nOut[3]: '2.0.0'\nIn [4]: tf.test.is_gpu_available()\n...\ntotalMemory: 3.95GiB freeMemory: 3.00GiB\n...\nOut[4]: True\n\n```\n\n \n\n\n# 配套TF2视频教程\n\n\u003Cp align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fstudy.163.com\u002Fcourse\u002FcourseMain.htm?share=2&shareId=480000001847407&courseId=1209092816&_trace_c_p_k2_=dca16f8fd11a4525bac8c89f779b2cfa\">\n    \u003Cimg src=\"res\u002Fcover.png\" width=\"400\">\n  \u003C\u002Fa>\n  \n  \u003Ca href=\"https:\u002F\u002Fstudy.163.com\u002Fcourse\u002FcourseMain.htm?share=2&shareId=480000001847407&courseId=1209092816&_trace_c_p_k2_=dca16f8fd11a4525bac8c89f779b2cfa\">\n    \u003Cimg src=\"res\u002FTF_QR_163.png\">\n  \u003C\u002Fa>\n\u003C\u002Fp> \n\nTensorFlow 2.0的视频教程链接：[深度学习与TensorFlow 2实战](https:\u002F\u002Fstudy.163.com\u002Fcourse\u002FcourseMain.htm?share=2&shareId=480000001847407&courseId=1209092816&_trace_c_p_k2_=dca16f8fd11a4525bac8c89f779b2cfa)\n\n\n# Acknowledgement\n- 爱可可-爱生活 友情推荐 ![](res\u002Fweibo.jpg)\n\n\n# Includes\n\n- TensorFlow 2.0 Overview\n- TensorFlow 2.0 Basic Usage\n- Linear Regression\n- MNIST, FashionMNIST\n- CIFAR10\n- Fully Connected Layer\n- VGG16\n- Inception Network\n- ResNet18\n- Naive RNN\n- LSTM\n- ColorBot\n- Auto-Encoders\n- Variational Auto-Encoders\n- DCGAN\n- CycleGAN\n- WGAN\n- Pixel2Pixel\n- Faster RCNN\n- A2C\n- GPT\n- BERT\n- GCN\n\nFeel free to submit a **PR** request to make this repo. more complete!\n \n\n\n# Refered Repos.\n\nOur work is not built from scratch. Great appreciation to these open works！\n\n- https:\u002F\u002Fgithub.com\u002Fmadalinabuzau\u002Ftensorflow-eager-tutorials\n- https:\u002F\u002Fgithub.com\u002Fherbiebradley\u002FCycleGAN-Tensorflow\n- https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftensorflow\u002Fblob\u002Fmaster\u002Ftensorflow\u002Fcontrib\u002Feager\u002Fpython\u002Fexamples\u002Fpix2pix\u002Fpix2pix_eager.ipynb\n- https:\u002F\u002Fgithub.com\u002Fmoono\u002Ftf-eager-on-GAN\n- https:\u002F\u002Fgithub.com\u002FViredery\u002Ftf-eager-fasterrcnn\n- https:\u002F\u002Fgithub.com\u002Fgithub\u002Fgitignore\u002Fblob\u002Fmaster\u002FPython.gitignore\n\n\n","该项目提供了基于TensorFlow 2.x版本的教程和示例代码，涵盖了从基础到高级的各种深度学习模型。核心功能包括CNN、RNN、GAN、Auto-Encoders、Faster R-CNN、GPT及BERT等实例，并使用Jupyter Notebook进行展示，便于理解和实践。适合希望深入了解TensorFlow 2.0及其在计算机视觉、自然语言处理等领域应用的学习者和开发者。此外，项目还提供了配套的视频教程链接，进一步帮助用户掌握相关知识。","2026-06-11 03:24:40","top_topic"]