• 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...

  • 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...

  • A big welcome to Pryml: faster machine learning applications to production

    Why so much silence? Building a company! That's why :) I am building pryml, a platform that allows data scientists build their applications on data they cannot get access to. This is the first of a series of episodes in which I will speak about the techn...

  • The dark side of AI: bias in the machine (Ep. 92)

      This is the fourth and last episode of mini series "The dark side of AI". I am your host Francesco and I’m with Chiara Tonini from London. The title of today’s episode is Bias in the machine      C: Francesco, today we are starting with an infuriating ...

  • The dark side of AI: social media and the optimization of addiction

    Chamath Palihapitiya, former Vice President of User Growth at Facebook, was giving a talk at Stanford University, when he said this: “I feel tremendous guilt. The short-term, dopamine-driven feedback loops that we have created are destroying how society ...

  • Deep learning is easier when it is illustrated (with Jon Krohn) (Ep. 86)

    In this episode I speak with Jon Krohn, author of Deeplearning Illustrated a book that makes deep learning easier to grasp.  We also talk about some important guidelines to take into account whenever you implement a deep learning model, how to deal with ...

  • How to generate very large images with GANs (Ep. 85)

    Join the discussion on our Discord server In this episode I explain how a research group from the University of Lubeck dominated the curse of dimensionality for the generation of large medical images with GANs. The problem is not as trivial as it seems. ...

  • More powerful deep learning with transformers (Ep. 84)

    Some of the most powerful NLP models like BERT and GPT-2 have one thing in common: they all use the transformer architecture. Such architecture is built on top of another important concept already known to the community: self-attention.In this episode I ...

  • Top 4 reasons why reinforcement learning sucks (Ep. 83)

    We have seen agents playing Atari games or Alpha Go, doing financial trading and modeling natural language. After watching reinforcement learning agents doing great in some domains let me tell […]

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