[unable to retrieve full-text content]In this guide you’ll learn how to scope a computer vision project, what kind of source data you need to make it successful, what kind of tools fit your project best, and a whole lot more.
Original Post: What we learned labeling 1 million images
[unable to retrieve full-text content]The idea behind the dplyr package is to do one thing at a time. dplyr has separate functions for every task which make its implementation crisp and easy to understand.
Original Post: Next Generation Data Manipulation with R and dplyr
[unable to retrieve full-text content]Chris Albon has created and shared a way more cool way to reinforce your machine learning learning (not to be confused with learning reinforcement learning): the flashcard.
Original Post: Learning Machine Learning… with Flashcards
[unable to retrieve full-text content]The term Horn Clause Mining, similar to Rule Based Machine Learning or Inductive Logic Programming, is used to describe the inverse of this functionality. Given a large enough knowledge base, can we infer rules that describe the data accurately?
Original Post: Using GRAKN.AI to Detect Patterns in Credit Fraud Data
[unable to retrieve full-text content]This is a guide to the main differences between PyTorch and TensorFlow, intended to be useful for anyone considering starting a new project or making the switch from one deep learning framework to another.
Original Post: PyTorch or TensorFlow?
[unable to retrieve full-text content]Data science educator Jose Portilla provides this definitive guide on becoming a data scientist, which includes everything from resources for acquiring specific skills, to searching for the first job, to mastering the interview.
Original Post: How to Become a Data Scientist: The Definitive Guide
[unable to retrieve full-text content]In this post, we will try to gain a high-level understanding of how SVMs work. I’ll focus on developing intuition rather than rigor. What that essentially means is we will skip as much of the math as possible and develop a strong intuition of the working principle.
Original Post: Support Vector Machine (SVM) Tutorial: Learning SVMs From Examples
[unable to retrieve full-text content]This post is a collection of 6 separate posts of 7 steps a piece, each for mastering and better understanding a particular data science topic, with topics ranging from data preparation, to machine learning, to SQL databases, to NoSQL and beyond.
Original Post: 42 Steps to Mastering Data Science
[unable to retrieve full-text content]Most forget that SQL isn’t just about writing queries, which is just the first step down the road. Ensuring that queries are performant or that they fit the context that you’re working in is a whole other thing. This SQL tutorial will provide you with a small peek at some steps that you can go through to evaluate your query.
Original Post: How To Write Better SQL Queries: The Definitive Guide – Part 2
[unable to retrieve full-text content]Data cleaning can seem intimidating, but it’s not hard if you know the basic steps. That’s why we’re excited to announce our newest ebook, “The Ultimate Guide to Basic Data Cleaning”!
Original Post: The Ultimate Guide to Basic Data Cleaning