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    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 performance, resulting in...

    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...

    Getting started with k-means clustering in Python

    Imagine you are an accomplished marketeer establishing a new campaign for a product and want to find appropriate segments to target, or you are...

    Feature extraction and image classification using Deep Neural Networks and OpenCV

    In a previous blog post we talked about the foundations of Computer vision, the history and capabilities of the OpenCV framework, and how to make...

    Semi-uniform strategies for solving K-armed bandits

    In a previous blog post we introduced the K-armed bandit problem - a simple example of allocation of a limited set of resources over time and under...

    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...

    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...

    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,...

    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...

    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...

    Getting Data with Beautiful Soup

    Data is all around us, from the spreadsheets we analyse on a daily basis, to the weather forecast we rely on every morning or the webpages we read....

    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...

    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...

    Building a Named Entity Recognition model using a BiLSTM-CRF network

    In this blog post we present the Named Entity Recognition problem and show how a BiLSTM-CRF model can be fitted using a freely available annotated...

    Fitting Support Vector Machines via Quadratic Programming

    In this blog post we take a deep dive into the internals of Support Vector Machines. We derive a Linear SVM classifier, explain its advantages, and...

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