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Notebooks have always been a tool for incremental development of software ideas. Data scientists use Jupyter to journal their work, explore and experiment with novel algorithms, quickly sketch new approaches and immediately observe the outcomes.

However, when the time is ripe, software developers turn to classical IDEs (Integrated Development Environment)…

Deep Learning

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The Machine Learning and Deep Learning fields have seen massive growth in the past decade. This is mainly due to hardware advances and the abundance of data that we are now able to produce and collect.

With that, the need to scale out model training to more computational resources is…

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Contributing to an open-source project is probably the best way to learn about the technologies you are interested in, contribute to your favorite tool’s evolution, and showcase your abilities to future employees.

Moreover, you get to participate in an active, vibrant community, meet developers that share common interests and ideas…

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When we think of a Machine Learning (ML) project, we think about data acquisition and processing. We think about model development, fitting, evaluation, and deployment.

The process of building and training ML models has always been in the spotlight. There is a lot of talk about different Neural Network architectures…

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PyTorch started its humble journey in 2016 and quickly became the go-to tool for Deep Learning researchers. However, PyTorch is much more than a mere prototyping tool today. It has grown into a fully-fledged production-ready framework that is expanding its fanbase in the business sector.

And that is precisely the…

Dimitris Poulopoulos

Machine Learning Engineer. I talk about AI, MLOps, and Python programming. More about me: www.dimpo.me

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