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 The hyperparameter optimization problem has been solved in many different ways for classical machine learning algorithms. Some examples include the use of grid search, random search, Bayesian optimization, meta-learning, and
Data Science Measuring Text Similarity Using the Levenshtein Distance In word processing or text chat applications, it's common that users make some unintended spelling mistakes. It could be as simple as writing "helo" (single "l") rather than "hello". Luckily,
Series: GauGAN Understanding GauGAN Part 1: Unraveling Nvidia's Landscape Painting GANs One of the most interesting papers presented at CVPR in 2019 was Nvidia's Semantic Image Synthesis with Spatially-Adaptive Normalization. This features their new algorithm, GauGAN, which can effectively turn doodles
Series: Optimization Intro to optimization in deep learning: Gradient Descent An in-depth explanation of Gradient Descent, and how to avoid the problems of local minima and saddle points.
