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    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 the task at hand. As...

    Data Science

    The Importance of Machine Learning Model Validation and How It Works

    Model valuation 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...

    Explaining black-box models using attribute importance, PDPs, and LIME

    In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights...
    Code

    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,...

    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...
    Machine Learning

    KNN with Examples in Python

    In this article, we will introduce and implement k-nearest neighbours (KNN) as one of the supervised machine learning algorithms. KNN is utilised to...

    Supervised vs. Unsupervised Learning: What’s the Difference?

    Of all the thousands of algorithms available for machine learning, or ML, the vast majority use one of three main branches of learning techniques.  ...
    Practical Techniques

    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....

    Accelerating model velocity through Snowflake Java UDF integration

    Over the next decade, the companies that will beat competitors will be “model-driven” businesses. These companies often undertake large data...
    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

    How To Deploy and Monitor Machine Learning Models

    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...

    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,...

    Evaluating Generative Adversarial Networks (GANs)

    This article provides concise insights into GANs to help data scientists and researchers assess whether to investigate GANs further. If you are...
    Engineering

    Production Data Science: Delivering Models with R Markdown

    R Markdown is one of those indispensable tools in a data scientist’s toolbox that provides speed and flexibility with the last-mile problem of...

    Techniques for Collecting, Prepping, and Plotting Data: Predicting Social Media-Influence in the NBA

    This article provides insight on the mindset, approach, and tools to consider when solving a real-world ML problem. It covers questions to consider...

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