![]() What tutorials are the most consistently upvoted? How to apply deep learning to their datasets.Īfter that, go over to popular computer science sub-reddits such as /r/machinelearning. Then, hop on large LinkedIn groups related to computer vision and machine learning. Go to the vast majority of popular machine learning and computer vision conferences and look at the recent list of publications. I have news for you: Deep learning is no different. When each of these methods were introduced, researchers and practitioners were equipped with new, powerful techniques - in essence, they were given a hammer and every problem looked like a nail, when in reality, all they needed was a few simple turns of a phillips head to solve a particular the problem. Despite the antagonizing title, the overall theme of this post centered around various trends in machine learning history, such as Neural Networks (and how research in NNs almost died in the 70-80’s), Support Vector Machines, and Ensemble methods. I once wrote a (controversial) blog post on getting off the deep learning bandwagon and getting some perspective. ![]() Looking for the source code to this post? Jump Right To The Downloads Section k-NN classifier for image classificationĪfter getting your first taste of Convolutional Neural Networks last week, you’re probably feeling like we’re taking a big step backward by discussing k-NN today. We’ll be using this dataset a lot in future blog posts (for reasons I’ll explain later in this tutorial), so make sure you take the time now to read through this post and familiarize yourself with the dataset.Īll that said, let’s get started implementing k-NN for image classification to recognize dogs vs. Cats dataset, as the name suggests, is to classify whether a given image contains a dog or a cat. Cats dataset, a subset of the Asirra dataset from Microsoft. We’ll then apply k-NN to the Kaggle Dogs vs. ![]() In the remainder of this blog post, I’ll detail how the k-NN classifier works. In fact, k-NN is so simple that it doesn’t perform any “learning” at all! To start, we’ll reviewing the k-Nearest Neighbor (k-NN) classifier, arguably the most simple, easy to understand machine learning algorithm. Now that we’ve had a taste of Deep Learning and Convolutional Neural Networks in last week’s blog post on LeNet, we’re going to take a step back and start to study machine learning in the context of image classification in more depth. Click here to download the source code to this post
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