TensorFlow is a power tool for building neural networks in many data science problems from tabular data and spreadsheets to NLP or text data and even images and photographs.
TensorFlow has 3 available APIs to build your neural network Sequential API, Functional API, and the Subclassing Model API. These 3 API offer different levels of flexibility and ease of writing code. The Sequential API is the easiest to use and a great place to start for some new to TensorFlow. It's limited in the sense that it can only have a one-way flow through the network. Where the functional API, although still fairly simple to write, allows having multiple inputs and outputs and really allows for creative neural network design.
The Subclassing Model API allows for the highest level of customization. With the Subclassing API you can you can define your own forward pass logic, loss functions, and optimization routines. This is also a good choice when we have a network that needs to adapt to the data because it allows for dynamic architecture. If you are a research scientist this is likely the API you'll be using. Don't worry we're just going to introduce the model API here as it is the most complex code to use and requires advanced Python knowledge.
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