You can master Computer Vision, Deep Learning, and OpenCV - PyImageSearch
Today’s blog post is a followup to a tutorial I did a couple of years ago on finding the brightest spot in an image. My previous tutorial assumed there was only one bright spot in the image that you wanted…
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In the previous section, we discussed gradient descent, a first-order optimization algorithm that can be used to learn a set of classifier weights for parameterized learning. However, this “vanilla” implementation of gradient descent can be prohibitively slow to run on…
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Nearly all of deep learning is powered by one very important algorithm: Stochastic Gradient Descent (SGD). — Goodfellow, Bengio, and Courville (2016) At this point, we have a strong understanding of the concept of parameterized learning. We previously discussed the…
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Over the past few months I’ve gotten quite the number of requests landing in my inbox to build a bubble sheet/Scantron-like test reader using computer vision and image processing techniques. A dataset of filled-in bubble sheets is essential for this…
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If you’ve been following along with this series of blog posts, then you already know what a huge fan I am of Keras. Keras is a super powerful, easy to use Python library for building neural networks and deep learning…
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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…
Read More of How to tune hyperparameters with Python and scikit-learn
Normally, I only publish blog posts on Monday, but I’m so excited about this one that it couldn’t wait and I decided to hit the publish button early. You see, just a few days ago, François Chollet pushed three Keras…
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In today’s blog post, we are going to implement our first Convolutional Neural Network (CNN) — LeNet — using Python and the Keras deep learning package. The LeNet architecture was first introduced by LeCun et al. in their 1998 paper,…
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I’m going to start today’s blog post by asking a series of questions which will then be addressed later in the tutorial: What are image convolutions? What do they do? Why do we use them? How do we apply them?…
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