Deep Learning Distilling knowledge from Neural Networks to build smaller and faster models This article discusses GPT-2 and BERT models, as well using knowledge distillation to create highly accurate models with fewer parameters than their teachers
Data Science Naïve Bayes for Machine Learning – From Zero to Hero Bayes’ Theorem is about more than just conditional probability, and Naive Bayes is a flavor of the theorem which adds to its complexity and usefulness.
Deep Learning When Not to Choose the Best NLP Model The world of NLP already contains an assortment of pre-trained models and techniques. This article discusses how to best discern which model will work for your goals.
Deep Learning N-Shot Learning: Learning More with Less Data Is it possible to use machine learning with small data? Yes, it is! Here's N-Shot Learning.
Deep Learning Gated Recurrent Unit (GRU) With PyTorch The Gated Recurrent Unit (GRU) is the newer version of the more popular LSTM. Let's unveil this network and explore the differences between these 2 siblings.
Deep Learning Becoming One With the Data This article discusses effective ways of handling the data in machine learning projects.
Deep Learning How to plan and execute your ML and DL projects This article gives the readers a checklist to structure their machine learning (applies to deep ones too) projects in effective ways.
Machine Learning A Gentle Introduction to Text Summarization in Machine Learning Text summarization is a common problem in the fields of machine learning and natural language processing (NLP). In this article, we'll explore how to create a simple extractive text summarization algorithm.
Machine Learning Introduction to Anomaly Detection in Python Learn what anomalies are and several approaches to detect them along with a case study.
