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Deep LearningLearn how to successfully apply Deep Learning to Computer Vision projects using Keras, TensorFlow, OpenCV, and more with my free Deep Learning tutorials and guides.
A few months ago, I wrote a tutorial on how to classify images using Convolutional Neural Networks (specifically, VGG16) pre-trained on the ImageNet dataset with Python and the Keras deep learning library. The pre-trained networks inside of Keras are capable…
Read More of ImageNet: VGGNet, ResNet, Inception, and Xception with Keras
You may have heard me mention it in a passing comment on the PyImageSearch blog… Maybe I even hinted at it in a 1-on-1 email… Or perhaps you simply saw the writing on the wall due to the recent uptick…
Read More of I’m writing a book on Deep Learning and Convolutional Neural Networks (and I need your advice).
A few months ago I demonstrated how to install the Keras deep learning library with a Theano backend. In today’s blog post I provide detailed, step-by-step instructions to install Keras using a TensorFlow backend, originally developed by the researchers and…
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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…
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
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
We previously learned about the k-NN classifier — a machine learning model so simple that it doesn’t do any actual “learning” at all. We simply have to store the training data inside the model, and then predictions are made at…
Read More of An Intro to Linear Classification with Python
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…
Read More of ImageNet classification with Python and Keras
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