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Tensorflow2.0 🍎🍊 is delicious, just eat it! 😋😋
| Date | Stars |
|---|---|
| 2026-07-24 | 9931 |
| 2026-07-25 | 9931 |
| 2026-07-28 | 9931 |
| 2026-07-30 | 9931 |
| 2026-08-06 | 9931 |
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# How to eat TensorFlow2 in 30 days ?🔥🔥 Click here for [Chinese Version（中文版）](#30天吃掉那只-tensorflow2) **《10天吃掉那只pyspark》** * 🚀 github项目地址: https://github.com/lyhue1991/eat_pyspark_in_10_days * 🐳 和鲸专栏地址: https://www.heywhale.com/home/column/5fe6aa955e24ed00302304e0 【代码可直接fork后云端运行，无需配置环境】 **《20天吃掉那只Pytorch》** * 🚀 github项目地址: https://github.com/lyhue1991/eat_pytorch_in_20_days * 🐳 和鲸专栏地址: https://www.heywhale.com/home/column/5f2ac5d8af3980002cb1bc08 【代码可直接fork后云端运行，无需配置环境】 **《30天吃掉那只TensorFlow2》** * 🚀 github项目地址: https://github.com/lyhue1991/eat_tensorflow2_in_30_days * 🐳 和鲸专栏地址: https://www.heywhale.com/home/column/5d8ef3c3037db3002d3aa3a0 【代码可直接fork后云端运行，无需配置环境】 **极速通道** * 🚀 公众号 “**算法美食屋**” 后台回复暗号："**吃货来了**" * 😋 获取以上3套教程的jupyter notebook 源码文件以及全部数据集的百度云盘下载链接。 * https://mp.weixin.qq.com/s/ymLtH5BqlWAkpOmCLQOYxw ### 1. TensorFlow2 🍎 or Pytorch🔥 Conclusion first: **For the engineers, priority goes to TensorFlow2.** **For the students and researchers，first choice should be Pytorch.** **The best way is to master both of them if having sufficient time.** Reasons: * 1. **Model implementation is the most important in the industry. Deployment supporting tensorflow models (not Pytorch) exclusively is the present situation in the majority of the Internet enterprises in China.** What's more, the industry prefers the models with higher availability; in most cases, they use well-validated modeling architectures with the minimized requirements of adjustment. * 2. **Fast iterative development and publication is the most important for the researchers since they need to test a lot of new models. Pytorch has advantages in accessing and debugging comparing with TensorFlow2.** Pytorch is most frequently used in academy since 2019 with a large amount of the cutting-edge results. * 3. Overall, TensorFlow2 and Pytorch are quite similar in programming nowadays, so mastering one helps learning the other. Mastering both framework provides you a lot more open-sourced models and helps you switching between them. ```python ``` ### 2. Keras🍏 and tf.keras 🍎 Conclusion first: **Keras will be discontinued in development after version 2.3.0, so use tf.keras.** Keras is a high-level API for the deep learning frameworks. It help the users to define and training DL networks with a more intuitive way. The Keras libraries installed by pip implement this high-level API for the backends in tensorflow, theano, CNTK, etc. tf.keras is the high-level API just for Tensorflow, which is based on low-level APIs in Tensorflow. Most but not all of the functions in tf.keras are the same for those in Keras (which is compatible to many kinds of backend). tf.keras has a tighter combination to TensorFlow comparing to Keras. With the acquisition by Google, Keras will not update after version 2.3.0 , thus the users should use tf.keras from now on, instead of using Keras installed by pip. ```python ``` ### 3. What Should You Know Before Reading This Book 📖? **It is suggested that the readers have foundamental knowledges of machine/deep learning and experience of modeling using Keras or TensorFlow 1.0.** **For those who have zero experience of machine/deep learning, it is strongly suggested to refer to ["Deep Learning with Python"](https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438/ref=sr_1_1?dchild=1&keywords=Deep+Learning+with+Python&qid=1586194568&sr=8-1) along with reading this book.** ["Deep Learning with Python"](https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438/ref=sr_1_1?dchild=1&keywords=Deep+Learning+with+Python&qid=1586194568&sr=8-1) is written by François Chollet, the inventor of Keras. This book is based on Keras and has no machine learning related prerequisites to the reader. "Deep Learning with Python" is easy to understand as it uses various examples to demonstrate. **No mathematical equation is in this book since it focuses on cultivating the intuitive to the deep learning.**
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:54d2a7412e36a7e8, topic:tensorflow
