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TensorFlow 1.x and 2.x
• TensorBoard Probability is a framework for combining probabilistic models
and deep learning.
• TensorBoard Playground is a nice UI for visualizing, debugging, and
inspecting neural networks; it's especially effective for education.
• Accelerated Linear Algebra (XLA) is a domain-specific compiler for linear
algebra that optimizes TensorFlow computations.
• MLPerf is an ML benchmark suite for measuring the performance of ML
software frameworks, ML hardware accelerators, and ML cloud platforms.
• Colab is a free Jupyter Notebook environment that requires no setup and
runs entirely in the cloud. Users can write and execute code, save and share
analyses, and access powerful computing resources, all for free from a
browser.
• TensorFlow Datasets is an official collection of datasets included in
TensorFlow. In addition, there are datasets freely available via Google
Research and a Google-powered Dataset Search (https://toolbox.google.
com/datasetsearch).
The list of resources, libraries, and tools is very long, and each tool would require
a separate chapter (in some cases a separate book!). In this book, we use Colab,
TensorBoard, Hub, and Lite and leave to the reader the task of checking out the
others online. Researchers can also use the TensorFlow Research Cloud (TFRC)
program, which allows you to apply for access to a cluster of more than 1,000
Cloud TPUs.
Models, datasets, tools, libraries, and extensions can be found
at https://www.tensorflow.org/resources/tools.
Language bindings
Python is the language of choice for TensorFlow. However, there are bindings
with many other languages, including JavaScript, C++, Java, Go, and Swift. The
community is also maintaining bindings for other languages, including C#, Haskell,
Julia, Ruby, Rust, and Scala. Normally the C API is used to build bindings for other
languages.
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