Tag Archives | dogs and cats

Understanding regularization for image classification and machine learning

In previous tutorials, I’ve discussed two important loss functions: Multi-class SVM loss and cross-entropy loss (which we usually refer to in conjunction with Softmax classifiers). In order to to keep our discussions of these loss functions straightforward, I purposely left out an important component: regularization. While our loss function allows us to determine how well (or poorly) our […]

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An intro to linear classification with Python

Over the past few weeks, we’ve started to learn more and more about machine learning and the role it plays in computer vision, image classification, and deep learning. We’ve seen how Convolutional Neural Networks (CNNs) such as LetNet can be used to classify handwritten digits from the MNIST dataset. We’ve applied the k-NN algorithm to classify whether or […]

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How to tune hyperparameters with Python and scikit-learn

In last week’s post, I introduced the k-NN machine learning algorithm which we then applied to the task of image classification. Using the k-NN algorithm, we obtained 57.58% classification accuracy on the Kaggle Dogs vs. Cats dataset challenge: The question is: “Can we do better?” Of course we can! Obtaining higher accuracy for nearly any machine learning algorithm […]

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