Tensorflow 2.0 很美味,直接吃吧!
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《10天吃掉那只pyspark》
《20天吃掉那只Pytorch》
《30天吃掉那只TensorFlow2》
极速通道
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:
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.
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" along with reading this book.
"Deep Learning with Python" 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.
This is a introduction reference book which is extremely friendly to human being. The lowest goal of the authors is to avoid giving up due to the difficulties, while "Don't let the readers think" is the highest target.
This book is mainly based on the official documents of TensorFlow together with its functions.
However, the authors made a thorough restructuring and a lot optimizations on the demonstrations.
It is different from the official documents, which is disordered and contains both tutorial and guidance with lack of systematic logic, that our book redesigns the content according to the difficulties, readers' searching habits, and the architecture of TensorFlow. We now make it progressive for TensorFlow studying with a clear path, and an easy access to the corresponding examples.
In contrast to the verbose demonstrating code, the authors of this book try to minimize the length of the examples to make it easy for reading and implementation. What's more, most of the code cells can be used in your project instantaneously.
Given the level of difficulty as 9 for learning Tensorflow through official documents, it would be reduced to 3 if learning through this book.
This difference in difficulties could be demonstrated as the following figure:
(1) Study Plan
The authors wrote this book using the spare time, especially the two-month unexpected "holiday" of COVID-19. Most readers should be able to completely master all the content within 30 days.
Time required everyday would be between 30 minutes to 2 hours.
This book could also be used as library examples to consult when implementing machine learning projects with TensorFlow2.
Click the blue captions to enter the corresponding chapter.
(2) Software environment for studying
All the source codes are tested in jupyter. It is suggested to clone the repository to local machine and run them in jupyter for an interactive learning experience.
The authors would suggest to install jupytext that converts markdown files into ipynb, so the readers would be able to open markdown files in jupyter directly.
#For the readers in mainland China, using gitee will allow cloning with a faster speed
#!git clone https://gitee.com/Python_Ai_Road/eat_tensorflow2_in_30_days
#It is suggested to install jupytext that converts and run markdown files as ipynb.
#!pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -U jupytext
#It is also suggested to install the latest version of TensorFlow to test the demonstrating code in this book
#!pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -U tensorflow
import tensorflow as tf
#Note: all the codes are tested under TensorFlow 2.1
tf.print("tensorflow version:",tf.__version__)
a = tf.constant("hello")
b = tf.constant("tensorflow2")
c = tf.strings.join([a,b]," ")
tf.print(c)
tensorflow version: 2.1.0
hello tensorflow2
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gitbook电子书地址： https://lyhue1991.github.io/eat_tensorflow2_in_30_days
github项目地址：https://github.com/lyhue1991/eat_tensorflow2_in_30_days
kesci专栏地址：https://www.kesci.com/home/column/5d8ef3c3037db3002d3aa3a0
极速通道
先说结论:
如果是工程师，应该优先选TensorFlow2.
如果是学生或者研究人员，应该优先选择Pytorch.
如果时间足够，最好TensorFlow2和Pytorch都要学习掌握。
理由如下：
暂无开放 Issues，或尚未同步最近议题。