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
Coronavirus Fighting COVID-19 with Data and AI: A Review of Active Research Groups and Datasets The economy has come to a halt; people are quarantined; work is stagnating; and governments fear the public health crisis this could turn into. The deadly coronavirus (SARS-CoV-2, or COVID-19
Data Science Introduction to Naive Bayes: A Probability-Based Classification Algorithm Imagine this: an electricity operator would like to supply specific units of electric current to various factory divisions based on their past trends of power consumption. To simplify the process,
Series: Ensemble Methods Gradient Boosting In Classification: Not a Black Box Anymore! Machine learning algorithms require more than just fitting models and making predictions to improve accuracy. Most winning models in the industry or in competitions have been using Ensemble Techniques or
Series: Ensemble Methods A Guide to AdaBoost: Boosting To Save The Day Today, machine learning is the premise of big innovations and promises to continue enabling companies to make the best decisions through accurate predictions. But what happens when the error susceptibility
Series: Ensemble Methods A Guide To Random Forests: Consolidating Decision Trees The Random Forest algorithm is one of the most popular machine learning algorithms that is used for both classification and regression. The ability to perform both tasks makes it unique,
Series: Ensemble Methods An Introduction to Decision Trees Decision Trees are the foundation for many classical machine learning algorithms like Random Forests, Bagging, and Boosted Decision Trees. They were first proposed by Leo Breiman, a statistician at the
Series: Ensemble Methods Introduction to Bagging and Ensemble Methods The bias-variance trade-off is a challenge we all face while training machine learning algorithms. Bagging is a powerful ensemble method which helps to reduce variance, and by extension, prevent overfitting.
Deep Learning Deploying Deep Learning Models Part 2: Hosting on Paperspace Gradient is a Paperspace product that simplifies developing, training, and deploying deep learning models. The platform provides infrastructure automation and a software development kit for machine learning developers. For creating
Deep Learning Deploying Deep Learning Models Part 1: Preparing the Model Whether you're working locally or on the cloud, many machine learning engineers don't have experience actually deploying their models so that they can be used on a global scale. In
