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Category: Deep Learning

#003 TF 2.0 Eager Execution- A Pythonic way of using TensorFlow

TensorFlow uses Eager execution, which is a more convenient way to execute the code, and also more “Pythonic”. It is a default choice in the latest version TensorFlow 2.0. In TensorFlow 1.x, we first need to write a Python program that constructs a graph for our computation, the program then invokes Session.run(), which hands the graph off for execution to the C++ runtime. This type of programming is called declarative programming (specification of the computation…
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#002 TF 2.0 An Introduction to TensorFlow 2.0

Highlights: In this post we are going to talk more about what are TensorFlow data model elements. Those are elements like Constants and Variables. So let’s see how we can create an operation like \(c = a*b\) and run it with the following lines of code. TensorFlow data model elements There are certain programming elements in TensorFlow that are essential for writing any TensorFlow code like Constants and Variables. These data model elements are used…
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#001 TF 2.0 An Introduction to TensorFlow 2.0

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

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

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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