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    A Detailed Guide To Transfer Learning and How It Works

    For data science teams working with inadequate data or too much data and not enough time or resources to process it, transfer learning can represent a significant shortcut in...

    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...

    Machine Learning Model Training: What It Is and Why It’s Important

    Training a machine learning (ML) model is a process in which a machine learning algorithm is fed with training data from which it can learn. ML...

    A Guide to Machine Learning Model Development and Production

    Machine learning is a subset of artificial intelligence (AI) that uses algorithms to learn from trends, data sets and certain behaviors. This process...

    Reinforcement Learning Introduction: Foundations and Applications

    Introduction When we think about learning, we are often tempted to focus on formal education that takes place during childhood and adolescence. We...

    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...

    ML internals: Synthetic Minority Oversampling (SMOTE) Technique

    In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed. We present the...

    Credit Card Fraud Detection using XGBoost, SMOTE, and threshold moving

    In this article, we'll discuss the challenge organizations face around fraud detection, how machine learning can be used to identify and spot...

    On-Demand Spark clusters with GPU acceleration

    Apache Spark has become the de facto standard for processing large amounts of stationary and streaming data in a distributed fashion. The addition...

    Choosing the right Machine Learning Framework

    Machine learning (ML) frameworks are interfaces that allow data scientists and developers to build and deploy machine learning models faster and...