Machine Learning - FloydHub Blog

Best Machine Learning Books (Updated for 2020)

The list of the best machine learning & deep learning books for 2020.

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.