Archive | Deep Learning

Stochastic Gradient Descent (SGD) with Python

In last week’s blog post, we discussed gradient descent, a first-order optimization algorithm that can be used to learn a set of classifier coefficients for parameterized learning. However, the “vanilla” implementation of gradient descent can be prohibitively slow to run on large datasets — in fact, it can even be considered computationally wasteful. Instead, we should apply Stochastic […]

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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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ImageNet classification with Python and Keras

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 models (VGG16, VGG19, and ResNet50) online — these networks are pre-trained on the ImageNet dataset, meaning that they can recognize 1,000 […]

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LeNet – Convolutional Neural Network in Python

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, Gradient-Based Learning Applied to Document Recognition. As the name of the paper suggests, the authors’ implementation of LeNet was used […]

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Compiling OpenCV with CUDA support

Alight, so you have the NVIDIA CUDA Toolkit and cuDNN library installed on your GPU-enabled system. What next? Let’s get OpenCV installed with CUDA support as well. While OpenCV itself doesn’t play a critical role in deep learning, it is used by other deep learning libraries such as Caffe, specifically in “utility” programs (such as building a dataset […]

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