Sean Moriarity @seanmor5
edited by Tammy Coron @Paradox927
Stable Diffusion, ChatGPT, Whisper—these are just a few examples of incredible applications powered by developments in machine learning. Despite the ubiquity of machine learning applications running in production, there are only a few viable language choices for data science and machine learning tasks. Elixir’s Nx project seeks to change that. With Nx, you can leverage the power of machine learning in your applications, using the battle-tested Erlang VM in a pragmatic language like Elixir. In this book, you’ll learn how to leverage Elixir and the Nx ecosystem to solve real-world problems in computer vision, natural language processing, and more.
The Elixir Nx project aims to make machine learning possible without the need to leave Elixir for solutions in other languages. And even if concepts like linear models and logistic regression are new to you, you’ll be using them and much more to solve real-world problems in no time.
Start with the basics of the Nx programming paradigm—how it differs from the Elixir programming style you’re used to and how it enables you to write machine learning algorithms. Use your understanding of this paradigm to implement foundational machine learning algorithms from scratch. Go deeper and discover the power of deep learning with Axon. Unlock the power of Elixir and learn how to build and deploy machine learning models and pipelines anywhere. Learn how to analyze, visualize, and explain your data and models.
Discover how to use machine learning to solve diverse problems from image recognition to content recommendation—all in your favorite programming language.
Sean Moriarity is author of Genetic Algorithms in Elixir: Solve Problems using Evolution, co-creator of the Nx library, and creator of the Axon deep learning framework. Sean’s interests include mathematics, machine learning, and artificial intelligence.
- Full details: Machine Learning in Elixir: Learning to Learn with Nx and Axon by Sean Moriarity
- View this book’s portal and details on how to post errata and suggestions here.
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