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One of my favorite things about TensorFlow 2.0 is that it brings high-level UX and low-level flexibility together fluently. This is the result of 4 years of watching practitioners and researchers use the product, and thinking about how we can make life better for both.
You no longer have on one hand, a high-level API that's easy to use but inflexible, & on the other hand a low-level API that's flexible but only approachable by experts. Instead, you have a spectrum of workflows, from the high-level to the low-level. And they're all compatible.pic.twitter.com/8smH0rl4d7
You have an array of ways to build models, each representing a certain level of trade-off between usability and flexibility. You can get started easily and then gradually dive into workflows where you're writing more and more logic from scratch.pic.twitter.com/Tx6k14AXzE
The same apply to model training as well -- it can be as simple as `fit` or as advanced as writing a training loop where you are responsible for every last detail.pic.twitter.com/wCYvk7t1Za
You can know very little about ML and get started in minutes -- and you'd be using objects in every way compatible with the workflow of a researcher building the next AlphaGo.
Thanks for the great work. How will Swift fit into this workflows? What is the idea of Swift for the future? Completely replace Python?
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