Chapter 7. Hello, TensorFlow!
The TensorFlow project is bigger than you might realize. The fact that it’s a library for deep learning and its connection to Google have helped TensorFlow attract a lot of attention. But beyond the hype, there are unique elements to the project that are worthy of closer inspection:
- The core library is suited to a broad family of machine learning techniques, not “just” deep learning.
- Linear algebra and other internals are prominently exposed.
- In addition to the core machine learning functionality, TensorFlow also includes its own logging system, its own interactive log visualizer, and even its own heavily engineered serving architecture.
- The execution model for TensorFlow differs from Python’s scikit-learn, or most tools in R.
Cool stuff, but—especially for someone hoping to explore machine learning for the first time—TensorFlow can be a lot to take in.
How does TensorFlow work? Let’s break it down so we can see and understand every moving part. We’ll explore the data flow graph that defines the computations your data will undergo, how to train models with gradient descent using TensorFlow, and how TensorBoard can visualize your TensorFlow work. The examples here won’t solve industrial machine learning problems, but they’ll help you understand the components underlying everything built with TensorFlow, including whatever you build next!
Names and Execution in Python and TensorFlow
The way TensorFlow manages computation is not totally ...
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