RNN Advanced Recurrent Neural Networks: Deep RNNs This series gives an advanced guide to different recurrent neural networks (RNNs). You will gain an understanding of the networks themselves, their architectures, applications, and how to bring the models
Reinforcement Learning A Thorough Introduction to Reinforcement Learning Access to humongous amounts of data and the availability of enormous computational power have led us to explore various techniques to draw out useful patterns and lead better lives. Churning
Machine Learning How to Apply Bayesian Decision Theory in Machine Learning In a previous article we discussed the theory behind Bayesian Decision Theory in detail. In this article we'll see how to apply Bayesian Decision Theory to different classification problems. We'll
Machine Learning Bayesian Decision Theory Explained Bayesian Decision Theory is the statistical approach to pattern classification. It leverages probability to make classifications, and measures the risk (i.e. cost) of assigning an input to a given
Machine Learning Beginner's Guide to Boltzmann Machines in PyTorch As research progressed and researchers could bring in more evidence about the architecture of the human brain, connectionist machine learning models came into the spotlight. Connectionist models, which are also
Machine Learning Beginner's Guide to Quantum Machine Learning As a Data Scientist and Researcher, I always try to find answers to the problems I come across every day. Working on real-world problems, I have faced many complexities both
Computer Vision Faster R-CNN Explained for Object Detection Tasks This article gives a review of the Faster R-CNN model developed by a group of researchers at Microsoft. Faster R-CNN is a deep convolutional network used for object detection, that
Computer Vision Paper Review: Funnel Activation for Visual Recognition (ECCV 2020) Component-based research in computer vision has accelerated significantly over the recent years, especially with the advent of adversarial attacks and attention mechanisms. One of the primary components which has been
Deep Learning How JavaScript Libraries Are Training Neural Networks on Web Browsers For years, JavaScript has been one of the most-loved programming languages by developers. It’s primarily used for creating web browser UI's and backend business logic (with Node.js). Looking
Deep Learning Evaluating Object Detection Models Using Mean Average Precision (mAP) To evaluate object detection models like R-CNN and YOLO, the mean average precision (mAP) is used. The mAP compares the ground-truth bounding box to the detected box and returns a
Deep Learning Evaluating Deep Learning Models: The Confusion Matrix, Accuracy, Precision, and Recall In computer vision, object detection is the problem of locating one or more objects in an image. Besides the traditional object detection techniques, advanced deep learning models like R-CNN and
Computer Vision Transpose Convolution Explained for Up-Sampling Images Convolutional neural networks need no introduction when it comes to image processing using Deep Neural Networks (DNNs). CNNs provide a more realistic way to extract and learn features from an
Computer Vision Channel Attention and Squeeze-and-Excitation Networks (SENet) In this article we will cover one of the most influential attention mechanisms proposed in computer vision: channel attention, as seen in Squeeze-and-Excitation Networks (SENet).
Computer Vision GhostNet (CVPR 2020) in PyTorch and TensorFlow In this post we'll take an in-depth look at feature maps in convolutional neural networks, do a thorough review of GhostNet, and break down the code in PyTorch and TensorFlow.
Computer Vision Image Super-Resolution: A Comprehensive Review Image Super Resolution refers to the task of enhancing the resolution of an image from low-resolution (LR) to high (HR). It is popularly used in the following applications: Surveillance: to
Reinforcement Learning Play Super Mario Bros with a Double Deep Q-Network As cool as neural networks are, the first time that I felt like I was building true AI was not when working on image classification or regression problems, but when
Neural Network Neural Architecture Search Part 4: REINFORCE Gradient and Evaluation So far we've built two major components of our neural architecture search (NAS) pipeline. In the second part of this series we created a model generator which takes encoded sequences
Deep Learning A Review of Popular Deep Learning Architectures: DenseNet, ResNeXt, MnasNet, and ShuffleNet v2 The aim of this three-part series has been to shed light on the landscape and development of deep learning models that have defined the field and improved our ability to
Deep Learning A Review of Popular Deep Learning Architectures: ResNet, InceptionV3, and SqueezeNet Previously we looked at the field-defining deep learning models from 2012-2014, namely AlexNet, VGG16, and GoogleNet. This period was characterized by large models, long training times, and difficulties carrying over
Deep Learning A Review of Popular Deep Learning Architectures: AlexNet, VGG16, and GoogleNet Problems ranging from image recognition to image generation and tagging have benefited greatly from various deep learning (DL) architectural advancements. Understanding the intricacies of different DL models will help you
Deep Learning Geometric Deep Learning Library Comparison This article covers an in-depth comparison of different geometric deep learning libraries, including PyTorch Geometric, Deep Graph Library, and Graph Nets.
Deep Learning Introduction to Geometric Deep Learning Recent advances in computer vision have come mainly through novel deep learning approaches, hierarchical machine learning models that rely on large amounts of data to be trained on specific tasks.
Research Neural Architecture Search Part 1: An Overview With countless options to design neural networks, an effective architecture search algorithm would be game-changing. Here we look at the state of the art.
Data Science Measuring Text Similarity Using the Levenshtein Distance This tutorial works through a step-by-step example of how the Levenshtein distance is calculated using dynamic programming.
Series: GauGAN Understanding GauGAN Part 1: Unraveling Nvidia's Landscape Painting GANs In this article we explain what GauGANs are, and how their architecture and objective functions work. This is part of a series on Nvidia GauGANs.
