Continuing our discussion on computer vision (see this article for a detailed introduction to the field) we will build a Deep Learning model to classify a dog into one of 120 breeds from it’s image. For this, we will be using Google’s Tensorflow platform in Python.
Before we build the actual model, it is worth discussing the building blocks that underlie Convolutional Neural Networks. Each model is made up of several “layers” stacked together- each of which has a specific function. …
“Computer Vision” is an area of Machine Learning that deals with image recognition and classification. Computer Vision models can be developed to accomplish tasks like facial recognition, identifying which breed a dog belongs to and even identifying a tumor from CT scans: the possibilities are endless.
In a series of articles on the topic, I will explore some of the key concepts surrounding Computer Vision. In this article, I will provide some intuition around how computers process images, and how objects can be recognised. …
“Supervised Learning” refers to Machine Learning algorithms that learn how to predict an outcome from past knowledge. For instance if we want to train a model to predict the outcome of a game, we may feed it with player stats for the last 1000 games, telling it which team won each game. The model then “learns” how the player stats are related to the outcome of the game, and forms a relationship between the variables and the outcome. Once the model has learnt these relationships, we can give it player stats for upcoming games, and it will predict the outcome.
In this article, I will explore the use of Unsupervised Machine Learning to generate artist recommendations using data from Spotify. While there are many algorithms that could have been used for this purpose, the one considered here is the NearestNeighbours learner, implemented using Scikit Learn in Python.
Let’s dive right into it!
1. The Data
We will use data from the Spotify Web API (which can be found here). The dataset contains data spanning the period from 1921–2020, covering ~27000+ artists!