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Tag: #convolutionalneuralnetworks

#OD1 YOLO Object Detection

YOU ONLY LOOK ONCE Highlights: In this post we will learn about YOLO Object Detection system, and how to implement such system with Keras. About Yolo: Our unified architecture is extremely fast. Our base YOLO model processes imagesin real-time at 45 frames per second. A smaller version of the network, Fast YOLO,processes an astounding 155 frames per second … — You Only Look Once: Unified, Real-Time Object Detection, 2015 Tutorial Overview: This post covers the…
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FR 001 Face Recognition with Celebrities

Highlights: In the world today, there are a lot of visual data and it is important how we utilize and interpret this data. The project is more of an evolution between traditional algorithms and deep learning techniques. How accurately can we predict and find the correct name of the celebrity in a given image or video frame. Tutorial Overview: This post covers the following topics: What is a facial recognition system? Applications of face recognition.…
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# 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 And now we can plot some predictions, to see how our model works. Images for testing can be downloaded from the…
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#017 CNN Inception Network

\(Inception\enspace network \)  Motivation for the \(Inception\enspace network \): When designing a layer for a \(convnet \) we might have to choose between a \(1 \times 3 \) filter, a \(3 \times 3\) or \(5\times 5\) or maybe a pooling layer. An \(Inception\enspace network \) solves this by saying:“ Why shouldn’t we apply them all ? ”. This makes the network architecture more complicated, but remarkably improves performance as well. Let’s see how this works. An  architecture…
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#001 CNN Convolutional Neural Networks

Convolutional Neural Networks   What is Computer Vision?      Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do.                               Computer Vision is one of the fields of artificial intelligence that is rapidly progressing thanks to Deep Learning. Let’s see…
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