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# Category: PyTorch

Highlights: Face detection and alignment are correlated problems. Change in various poses, illuminations, and occlusions in unrestrained environments can make these problems even more challenging. In this tutorial, we will study how deep learning approaches can be great performing solutions for these two problems. We will study a deep cascaded multi-task framework proposed by Kaipeng Zhang [1] et al. that predicts face and landmark location in a coarse-to-fine manner. So let’s get started! Tutorial Overview:…

### #015 PyTorch – Building A Smile Detector with PyTorch

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…

### #011 Pytorch – RNN with PyTorch

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…
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…