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Getting started with AI

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Article from ADMIN 85/2025

By Jeff Layton

A great way to start writing code with AI is to use Keras, an open source easy-to-learn library that can use multiple frameworks.

I wanted to start learning about artificial intelligence (AI) and writing code, but I wasn't sure where to start. I didn't want to write in C++ or use a directed acyclic graph (DAG) or some other computer-science-centric language. I wanted to be able to write something straightforward, without complication, that allowed me to define clearly what I wanted to do.

The early years of AI saw the emergence of a wide range of frameworks. Most of them used Python, but all of them had their own language for reading the data, defining the model, and solving weights, which exposed my fears of learning a new "language." Which framework should I select and why? Was one a clear leader?

I didn't see any pointers that said, "go this way," and I didn't see any articles that explained the trade-offs, but I did read about Keras [1], and that is where I started. (Note that I can't write about the things I do or learn in my day job, but this subject I learned before joining.)

A Brief History of Keras

Keras was developed by François Chollet in 2014 out of necessity for an open source implementation of recurrent neural networks (RNNs) and the long short-term memory (LSTM) models that train them. Keras is Python based, and was first released in March 2015. It developed a following fairly quickly because the models were popular at the time.

Up to version 2.3, Keras supported multiple frameworks (back ends) that included TensorFlow, Microsoft Cognitive Toolkit (commonly referred to as CNTK), Theano, and PlaidML. By version 2.4, Keras only supported TensorFlow. In version 3.0 and subsequent versions, Keras once again supported multiple frameworks, including TensorFlow, PyTorch, and JAX.

Fundamentally, Keras abstracts away the details of the back-end frameworks so that you call functions from Keras to build and train

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