You can master Computer Vision, Deep Learning, and OpenCV - PyImageSearch
I publish a brand new Computer Vision, Deep Learning, and OpenCV tutorial every Monday at 10AM EST. Here you'll find my most recent tutorials and guides.
Over the past two years running the PyImageSearch blog, I’ve authored two tutorials detailing the required steps to install OpenCV (with Python bindings) on Ubuntu. You can find the two tutorials here: Install OpenCV 3.0 and Python 2.7+ on Ubuntu…
Read More of Ubuntu 16.04: How to install OpenCV
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…
Read More of Stochastic Gradient Descent (SGD) with Python
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…
Read More of Gradient Descent with Python
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…
Read More of Bubble sheet multiple choice scanner and test grader using OMR, Python, and OpenCV
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…
Read More of A simple neural network with Python and Keras
Many strategies used in machine learning are explicitly designed to reduce the test error, possibly at the expense of increased training error. These strategies are collectively known as regularization. —Goodfellow, Bengio, and Courville (2016) In previous lessons, we discussed two…
Read More of Understanding Regularization for Image Classification and Machine Learning
Last week, we discussed Multi-class SVM loss; specifically, the hinge loss and squared hinge loss functions. A loss function, in the context of Machine Learning and Deep Learning, allows us to quantify how “good” or “bad” a given classification function…
Read More of Softmax Classifiers Explained
A couple weeks ago,we discussed the concepts of both linear classification and parameterized learning. This type of learning allows us to take a set of input data and class labels, and actually learn a function that maps the input to…
Read More of Multi-class SVM Loss
Today’s blog post will take a short diversion from our recent trend of Deep Learning tutorials here on the PyImageSearch blog and instead focus on a topic that I’ve been receiving a ton of emails about lately — common errors…
Read More of Common errors using the Raspberry Pi camera module
