FloydHub Blog

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.

Data Science

A Pirate's Guide to Accuracy, Precision, Recall, and Other Scores

Once you've built your classifier, you need to evaluate its effectiveness with metrics like accuracy, precision, recall, F1-Score, and ROC curve.

Deep Learning

Introduction to Adversarial Machine Learning

Machine learning advancements lead to new ways to train models, as well as deceive them. This article discusses ways to train and defend against attacks.

Deep Learning

Training Neural Nets: a Hacker’s Perspective

This deep dive is all about neural networks - training them using best practices, debugging them and maximizing their performance using cutting edge research.

Deep Learning

Attention Mechanism

What is Attention, and why is it used in state-of-the-art models? This article discusses the types of Attention and walks you through their implementations.

Data Science

Multiprocessing vs. Threading in Python: What Every Data Scientist Needs to Know

This deep dive on Python parallelization libraries - multiprocessing and threading - will explain which to use when for different data scientist problem sets.

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.

Data Science

DIY Data: Web Scraping with Python and BeautifulSoup

Getting sufficient clean, reliable data is one of the hardest parts of data science. Web scraping automates the process of visiting web pages, downloading the data, and cleaning the results. With this technique, we can create new datasets from a large compendium of web pages.

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.

Data Science

Statistics for Data Science

The article elucidates the importance of statistics in the field of data science, wherein "Statistics" is imagined as a friend to a data scientist and their friendship is unraveled.

Deep Learning

Generative Adversarial Networks - The Story So Far

Generative adversarial networks (GANs) have been the go-to state of the art algorithm to image generation in the last few years. In this article, you will learn about the most significant breakthroughs in this field, including BigGAN, StyleGAN, and many more.

Deep Learning

Long Short-Term Memory: From Zero to Hero with PyTorch

Long Short-Term Memory (LSTM) Networks have been widely used to solve various sequential tasks. Let's find out how these networks work and how we can implement them.

Evolutionary Algorithm

Introduction to Genetic Algorithms

Genetic algorithms are a specific approach to optimization problems that can estimate known solutions and simulate evolutionary behavior in complex systems.

Deep Learning

Using Deep Learning and TensorFlow Object Detection API for Corrosion Detection and Localization

While computer vision techniques have been used with limited success for detecting corrosion from images, Deep Learning has opened up whole new possibilities

Reinforcement Learning

An introduction to Q-Learning: Reinforcement Learning

Learn about the basic concepts of reinforcement learning and implement a simple RL algorithm called Q-Learning.

Deep Learning

How to Build OpenAI's GPT-2: "The AI That Was Too Dangerous to Release"

The Key Insights Behind the Greatest Language Model of all Time

Deep Learning

A Beginner’s Guide on Recurrent Neural Networks with PyTorch

Learn the basics of Recurrent Neural Networks and build a simple Language Model with PyTorch

Introduction to K-Means Clustering in Python with scikit-learn

In this article, get a gentle introduction to the world of unsupervised learning and see the mechanics behind the old faithful K-Means algorithm.

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.

Reinforcement Learning

Meta-Reinforcement Learning

The general trend in machine learning research is to stop fine-tuning models, and instead use a meta-learning algorithm that automatically finds the best architecture and hyperparameters. What about meta-reinforcement learning (meta-RL)? Meta-RL is just meta-learning applied to RL.

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