Day 35: Iterative Loop of ML development
In the next few sections, we'll explore the process of developing a ML system. Let's take a look at the iterative loop of ML development:...
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Joan Kusuma
Nov 3, 20231 min read
Day 34: Bias/Variance and Neural Network
In our previous post, we see how by looking at our training error and cross validation error, we can try to get a sense of whether our...
Oct 31, 20238 min read
Fashion Recommender System with Visual Similarity Search
Introduction The objective of this project is to create a content-based fashion recommender system by utilizing vector embeddings of...
Oct 19, 20232 min read
Day 33: Establishing a baseline level of performance
Let's take a look at some concrete numbers for what train-error and cv-error might be and see how you can judge if a learning algorithm...
Oct 19, 20232 min read
Day 32: Bias and Variance
The typical workflow of developing a machine learning system is that you have an idea and you train the model, and you almost always find...
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