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Category: Machine Learning

#FA 003 Face Detection in Videos using OpenCV

In real case scenarios, there is often a need for detection and recognition of faces not just in images, but in videos. For example, this is a necessary prerequisite for security cameras, filters on social networks, identification at work and many other cases where cameras are used. The pictures are static, but videos can be seen as a series of pictures, or frames. All video clips are constructed of a constant number of frames in…
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Keras An implementation of a Convolutional Neural Network in Keras

# K An implementation of a Convolutional Neural Network in Keras – MNIST dataset

In this post we will see how we can classify handwritten digits using convolutional neural network implemented in Keras. Required packages: Numpy Matplotlib Keras Tensorflow Sklearn Seaborn __future__ Keras-vis Table of Contents: Load the digit dataset Implementing a Neural Network Visualization Test our model Save a model as picture Activation Maps Saliency maps Activations Now we can plot some predictions, to see how our model works. Images for testing can be downloaded from the internet…
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# K An implementation of a Shallow Neural Network in Keras – MNIST dataset

In this post we will see how we can classify handwritten digits using shallow neural network implemented in Keras. Our model will have 2 layers, with 64(height x width) neurons in the input layer and 10 neurons in the output layer.We will use normal initializer that generates tensors with a normal distribution. The optimizer we’ll use is Adam .It is an optimization algorithm that can be used instead of the classical stochastic gradient descent procedure…
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#027 CNN Non-Max Suppression algorithm

Non-Max Suppression In this post, we will learn how the non-max suppression algorithm allows us to overcome multiple detections of the same object in an image. Let’s go through an example! Let’s say we want to detect pedestrians, cars, and motorcycles in this image. If we look at the picture above we can see that there are two cars. Each of these two cars has one midpoint so it should be assigned to just one…
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#026 CNN Intersection over Union

Intersection over Union In this post, we will learn about a function called Intersection over union. We will use this to make our object detection algorithm to work even better. How do we tell if our object detection algorithm is working well? When doing the object detection our task is to localize the object in the best possible way. Take a look at the picture above we can see that there are two bounding boxes…
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