1. Haskell tools

    A very brief description of Haskell tools I've encountered so far.

  2. Like top, but for GPUs

    Monitoring GPU (Graphical Processing Unit) utilization for deep learning.

  3. Model interoperability

    How the trained models can be persisted and reused across libraries and environments.

  4. Productivity tools

    An overview of the tools and methods I use to organize my personal and work life.

  5. My ML learning path

    A story on how I learned myself some machine learning and an advice to fellow engineers.

  6. On the importance of software engineering in machine learning

    Machine learning is a software-heavy area where software development know-how is useful in a number of cases. Let's take a look at how applying engineering practices can help build ML products faster, make them more reliable and keep data scientists happy.

  7. Hello

    Welcome, stranger!