TensorFlow 2.0 officially released on September 30th, 2019. If you’re about to start a new ML project like me you may be a bit confused about which version of TensorFlow you should use. After all, Python 3 was released in 2008 and support for Python 2.7 will finally case on January 1st, 2020. Granted, that’s not a perfect comparison but TensorFlow 1.x has been around since 2015 and there are numerous articles, tutorials, and support forums to guide you to success using TensorFlow 1.x.\u00a0 I’m here to tell you that you should switch to TensorFlow 2.0 for your next machine learning project.<\/p>\n
In short, TensorFlow 2.0 is more user-friendly and easier to debug than TensorFlow 1.x. This all comes down to the fact that\u00a0TensorFlow 2.0 has eager execution by default. This means that you no longer have to stitch together the graph by making tf.*! In addition, there are no more globals meaning it is now easier to keep track of your variables and their scopes.\u00a0 Last, but certainly not least is that TensorFlow 2.0 will support legacy code from TensorFlow 1.x.<\/p>\n
With all that out of the way, it’s easy to see that TensorFlow 2.0 is a good choice for your next project. Now on to learning\u00a0TensorFlow 2.0.<\/p>\n
freeCodeCamp’s Tutorial<\/a><\/p>\n Coursera Course<\/a><\/p>\n MNIST W\/ Google Collab<\/a><\/p>\n