Highlights: People spend a lifetime in the pursuit of happiness. Fortunately, intelligent machines can, today, detect happiness or smiles within seconds using Smile Detection Models. In this tutorial post, we will learn how to build a Deep Learning-based Smile Detection model in PyTorch. We will utilize the LeNet-5 architecture and work on the CelebA dataset which is a large dataset of images containing faces of people smiling and not smiling, respectively. So let’s begin! Tutorial…
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Highlights: In this post, we will give a brief overview of Recurrent Neural Networks. Along with the basic understanding of the RNN model, we will also demonstrate how it can be implemented in PyTorch. We will use a sine wave, as a toy example signal, so it will be very easy to follow if you are encountering RNNs for the first time. Let’s start with our post! Tutorial Overview: Introduction to Recurrent Neural Networks Introduction…
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Highlights: Hello everyone. In this post, we will demonstrate how to build the Fully Connected Neural Network with a Multilayer perceptron class in Python using PyTorch. This is an illustrative example that will show how a simple Neural Network can provide accurate results if images from the dataset are converted into a vector. We are going to use a fully-connected ReLU Network with three layers, trained to predict the output \(y \) from given input…
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Highlights:  In the field of machine learning, the goal of classification is to use characteristics of an object to identify a class to which that object belongs. To classify the object we can use various types of classifiers. In this post, we are going to talk about one particular type of classifiers called Linear Classifiers that can be used to solve easy image classification problems. To better understand them we will conduct several experiments and…
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Highlights: Hello and welcome to our new blog post. So far, we have mainly worked with very simple datasets. To train our models we used linear regression and logistic regression models and for that purpose, we artificially created very easy toy data sets such as blobs. However, in order to fully utilize the power of Deep Learning and neural networks, we will have to work with the larger dataset. Now it is a great time…
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