Recent Posts

  • Rust and machine learning #4: practical tools (Ep. 110)

    In this episode I make a non exhaustive list of machine learning tools and frameworks, written in Rust. Not all of them are mature enough for production environments. I believe that community effort can change this very quickly. To make a comparison with...

  • Rust and machine learning #3 with Alec Mocatta (Ep. 109)

    In the 3rd episode of Rust and machine learning I speak with Alec Mocatta. Alec is a +20 year experience professional programmer who has been spending time at the interception of distributed systems and data analytics. He's the founder of two startups in...

  • Rust and machine learning #2 with Luca Palmieri (Ep. 108)

    In the second episode of Rust and Machine learning I am speaking with Luca Palmieri, who has been spending a large part of his career at the interception of machine learning and data engineering. In addition, Luca contributed to several projects closer t...

  • Rust and machine learning #1 (Ep. 107)

    This is the first episode of a series about the Rust programming language and the role it can play in the machine learning field. Rust is one of the most beautiful languages I have ever studied so far. I personally come from the C programming language, t...

  • Protecting workers with artificial intelligence (with Sandeep Pandya CEO 106)

    In this episode I have a chat with Sandeep Pandya, CEO at a company that uses sensor fusion, computer vision and more to provide safer working environments to workers in heavy industry.Sandeep is a senior executive who can hide the complexit...

  • Compressing deep learning models: rewinding (Ep.105)

    As a continuation of the previous episode in this one I cover the topic about compressing deep learning models and explain another simple yet fantastic approach that can lead to much smaller models that still perform as good as the original one. Don't fo...

  • Compressing deep learning models: distillation (Ep.104)

    Using large deep learning models on limited hardware or edge devices is definitely prohibitive. There are methods to compress large models by orders of magnitude and maintain similar accuracy during inference. In this episode I explain one of the first m...

  • Pandemics and the risks of collecting data (Ep. 103)

    Codiv-19 is an emergency. True. Let's just not prepare for another emergency about privacy violation when this one is over.   Join our new Slack channel   This episode is supported by Proton. You can check them out at or

  • Why average can get your predictions very wrong (ep. 102)

    Whenever people reason about probability of events, they have the tendency to consider average values between two extremes. In this episode I explain why such a way of approximating is wrong and dangerous, with a numerical example. We are moving our comm...

  • Activate deep learning neurons faster with Dynamic RELU (ep. 101)

    In this episode I briefly explain the concept behind activation functions in deep learning. One of the most widely used activation function is the rectified linear unit (ReLU). While there are several flavors of ReLU in the literature, in this episode I ...

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