TensorFlow vs TensorFlow.js: What are the differences?
TensorFlow and TensorFlow.js can be categorized as "Machine Learning" tools.
TensorFlow.js is an open source tool with 11.2K GitHub stars and 816 GitHub forks. Here's a link to TensorFlow.js's open source repository on GitHub.
Uber Technologies, 9GAG, and StyleShare Inc. are some of the popular companies that use TensorFlow, whereas TensorFlow.js is used by 8villages, ADEXT, and Taralite. TensorFlow has a broader approval, being mentioned in 200 company stacks & 135 developers stacks; compared to TensorFlow.js, which is listed in 5 company stacks and 3 developer stacks.
What is TensorFlow?
What is TensorFlow.js?
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What are the cons of using TensorFlow.js?
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Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:
At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.
TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.
Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:
(Direct GitHub repo: https://github.com/uber/horovod)
Machine Learning in EECS 445