9 Common Mistakes Beginner Data Scientists Make
Don't fall into these pitfalls!
Software Engineering Tips and Best Practices for Data Science
With great code comes great machine learning
A new portfolio for a New Year 🎉 when Gatsby meets Netlify !
This is my brand new portfolio built using modern web technologies such as Gatsby, React and GraphQL and continuous deployment on Netlify. Don't worry, the old articles have been migrated and new exciting things are coming up soon!
End to End Machine Learning: From Data Collection to Deployment 🚀
Learn how build an end to end machine learning application from scratch. To do this, we'll walk you through the process of collecting data, training a deep learning model, building a Dash application, putting everything in Docker and deploying to AWS. This post is a little bit longer than usual but the different parts are independant and reusable in other projects
Introduction to AutoML with MLBox 🤖
Learn about automated machine learning by using the MLBox package. This library will help you train a pipeline for a classification problem. It'll start off by loading and cleaning the data, removing drift, launching a robust pipeline of accelerated optimization, generating predictions and much more...
Introduction to Neural Networks and Deep Learning from scratch 💡
Learn about neural networks and implement them from scratch using Python only with no frameworks involved. Back-propagation will no longer be a mystery
Automate the diagnosis of Knee Injuries 🏥 with Deep Learning part 3: Interpret models' predictions
In this last post, we will focus on interpretability to assess what the ACL tear detector we trained in the previous article actually learnt. To do this, we'll explore a popular interpretability technique called Class Activation Map (CAM), applied when using convolutional neural networks that have a special architecture
Automate the diagnosis of Knee Injuries 🏥 with Deep Learning part 2: Building an ACL tear classifier
In this post, you'll learn how to use PyTorch to train an Anterior Cruciate Ligament (ACL) tear classifier that sucessfully detects these injuries from MRIs with a very high performance. We'll dive into the code and we'll go through various tips and tricks ranging from transfer learning to data augmentation, stacking and handling medical images
Automate the diagnosis of Knee Injuries 🏥 with Deep Learning part 1: an overview of the MRNet Dataset
This is a first post of a series dedicated to medical imaging using deep learning. Today, we'll explore an MRI dataset of knee injuries
Overview and benchmark of traditional and deep learning models in text classification 📝
How do deep learning models based on convolutions (CNNs) and recurrents cells (RNNs) compare to Bag of Words models in the case of a sentiment classification problem
Understanding deep Convolutional Neural Networks 👁 with a practical use-case in Tensorflow and Keras
Learn about Convolutional Neural Networks with a hands-on classification problem using Tensorflow and Keras
Sentiment analysis 👍 👎 on Twitter using Word2vec and Keras
Learn how to perform sentiment analysis on tweets using the word2vec embedding model and neural networks via Keras
How to mine newsfeed data 📰 and extract interactive insights in Python
Learn how to cluster and visualize news data using KMeans, LDA and interactive plotting with Bokeh
How to score 0.8134 🏅 in Titanic Kaggle Challenge
Learn how to tackle a kaggle competition from the beginning till the end through data exploration, feature engineering, model building and fine-tuning