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    Transformers - Self-Attention to the rescue

    If the mention of "Transformers" brings to mind the adventures of autonomous robots in disguise you are probably, like me, a child of the 80s: Playing with Cybertronians who...

    Data Science

    Polars - A lightning fast DataFrames library

    We have previously talked about the challenges that the latest SOTA models present in terms of computational complexity. We've also talked about...

    The Importance of Machine Learning Model Validation and How It Works

    Model validation is a core component of developing machine learning or artificial intelligence (ML/AI). While it’s separate from training and...

    MATLAB for Data Science and Machine Learning

    The opportunities to solve problems with the use of data are greater than ever, and as different industries embrace them, the available data has been...

    Data Exploration with Pandas Profiler and D-Tale

    We all have heard how data is the new oil. I always say that if that is the case, we need to go through some refinement process before that raw oil...
    Code

    Getting Started with OpenCV

    In this article we talk about the foundations of Computer vision, the history and capabilities of the OpenCV framework, and how to make your first...

    Reinforcement Learning: The K-armed bandit problem

    In a previous blog post we talked about the foundations of reinforcement learning. We covered classical and operant conditioning, rewards, states,...
    Machine Learning

    N-shot and Zero-shot learning with Python

      Introduction In a previous post we talked about how advances in deep learning neural networks have allowed significant improvements in learning...

    A Hands-on Tutorial for Transfer Learning in Python

    Fitting complex neural network models is a computationally heavy process, which requires access to large amounts of data. In this article we...
    Practical Techniques

    Building a Speaker Recognition Model

    The ability of a system to recognize a person by their voice is a non-intrusive way to collect their biometric information. Unlike fingerprint...

    Fundamentals of Signal Processing

    Basics of Digital Signal Processing A signal is defined as any physical quantity that varies with time, space or any other independent variables....
    Leaders at Work

    Fireside Chat: Stig Pedersen from Topdanmark

    "In having one or two very successful algorithmic deployments, the business then begins coming to you to ask for assistance. It becomes a mutual...

    Defining Metrics to Drive Machine Learning Model Adoption & Value

    One of the biggest ironies of enterprise data science is that although data science teams are masters at using probabilistic models and diagnostic...
    Model Management

    A Guide to Machine Learning Model Deployment

    Machine-learning (ML) deployment involves placing a working ML model into an environment where it can do the work it was designed to do. The process...

    Machine Learning Modeling: How It Works and Why It’s Important

    Models are the central output of data science, and they have tremendous power to transform companies, industries, and society.  At the center of...

    The Role of Containers on MLOps and Model Production

    Container technology has changed the way data science gets done. The original container use case for data science focused on what I call,...
    Engineering

    Tensorflow, PyTorch or Keras for Deep Learning

    Machine learning provides us with ways to create data-powered systems that learn and enhance themselves, without being specifically programmed for...

    Getting Started with Ray

    In this blog post we give a quick introduction to Ray. We talk about the architecture and execution model, and present some of Ray's core paradigms...

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