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Tag: machine learning

#001 TF 2.0 An Introduction to TensorFlow 2.0

23.12.2018

What is TensorFlow 2.0? TensorFlow is an open-source library for numerical computations built by Google Brain team. TensorFlow is based on the data flow graphs. Moreover, it actually allows developers to create data flow graphs—structures that describe how data moves through a graph, or a series of processing nodes. Each node in the graph represents a mathematical operation, and each connection or edge between nodes is a multidimensional data array or a tensor. If we…
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#032 CNN Triplet Loss

01.12.2018

In the last post, we talked about Siamese Network, but we didn’t talk how to actually define an objective function to make our neural network learn. So, in order to do that, here we will define Triplet Loss. Triplet Loss One way to learn the parameters of the neural network, which gives us a good encoding for our pictures of faces, is to define and apply gradient descent on the Triplet loss function. Let’s see…
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# 031 CNN Siamese Network

01.12.2018

Siamese Network The job of the function \(d\), which we presented in the previous post, is to use two faces and to tell us how similar or how different they are. A good way to accomplish this is to use a Siamese network. We get used to see pictures of \(convnets \), like these two networks in the picture below. We have an input image, denoted with \(x^{(1)}\), and through a sequence of \(Convolutional \),…
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#030 CNN One-Shot Learning

01.12.2018

One – Shot Learning Solving the one-shot problem represents a challenge of face recognition task. This means that for most face recognition applications we need to recognize a person having only a single image or given just one example of that person’s face. Typically, deep learning algorithms don’t work well if there is only one training example. However, we will show how this problem can be tackled.  Let’s say that we have a database of…
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#029 CNN Yolo Algorithm

26.11.2018

YOLO ALGORITHM In this post, we will finish with the theory behind object detection. We will combine last few posts together to complete the \(Yolo \) object detection algorithm. As the first step, let’s see how we can construct our training set. Let’s suppose that we are trying to train an algorithm to detect three objects: pedestrians, cars and motorcycles. In addition, we will need to explicitly define the background class, so we just have…
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