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
Paperspace 4K Streaming Lands in the Latest Paperspace Release! We're pleased to announce that the Paperspace native application now supports 4K streaming on all Dedicated GPU virtual machines. Note: 4K streaming is available in the latest version of the
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
Tutorial A Guide to Paperspace's Gradient Community Notebooks Gradient Community Notebooks allow users to create, run, and share Jupyter notebooks on free GPUs. In this post Gradient Community Notebooks will be introduced and the steps to get started
Neural Network Neural Architecture Search Part 3: Controllers and Accuracy Predictors In the first part of this series we saw an overview of neural architecture search, including a state of the art review of the literature. In Part 2 we then
Announcement Paperspace Joins NVIDIA DGX Program With Gradient, our developer-first ML platform, the guiding methodology has always been: How can we make developing ML models as easy as building a modern web app? And what tools can we build to enable software developers from different domains to be successful in machine learning and deep learning?
Machine Learning What is MLOps? The machine learning space is arguably one of the most advanced industries in the world and yet, ironically, the way it operates is reminiscent of software development in the '90s.
Announcement 🎁 Product Update: Code Diff View The code viewer in Gradient experiments now includes a visual diff tool targeted at developers to better visualize code changes within experiments. How it worksThe diff view helps you compare
Coronavirus Turning Noise Into Signal: Using AI to Gain Context for Scientific Research Josh Nicholson is the Co-Founder and CEO of scite (www.scite.ai), which is using deep learning to analyze the entirety of the scientific literature to better measure the veracity of scientific work.
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.
Features Managing Your Paperspace Core Machine in iOS The Paperspace iOS Shortcut is a handy tool for quickly managing your Core Machines on the go. No login required.
Neural Network Neural Architecture Search Part 2: Search Space, Architecture Design and One-Shot Training In the first part of the series, we took a look at all the different angles the problem of neural architecture is being approached from. With the foundation covered, we'll
Announcement 🎁 Product Update: Hotkeys We're excited to share the arrival of hotkey keyboard shortcuts! One of the things we hear often from our users is that they want to be able to switch contexts
Coronavirus COVID-19: The Data We Have, and How We Can Use It IntroductionMany of us currently feel trapped inside the confines of their homes, stuck between four walls and afraid to venture out into open spaces to avoid the spread of COVID-19.
Applications Using PoseNet To Create a Snapchat-Like Eye Filter for Android PoseNet is a computer vision deep learning model used for estimating a person's physical position. Based on MobileNet, it can be deployed to mobile devices and takes even less time
Applications Building an App for Eye Filters with PoseNet Pose estimation is a computer vision task for detecting the pose (i.e. orientation and position) of objects. It works by detecting a number of keypoints so that we can
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
Announcement Introducing Paperspace + Hugging Face 🤗 We're excited to announce a new collaboration with Hugging Face to provide state-of-the-art NLP tools to the community. Check out the Webinar, and run the new notebooks on a free
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,
Coronavirus Fighting Coronavirus With AI, Part 1: Improving Testing with Deep Learning and Computer Vision This post will cover how testing is done for the coronavirus, why it's important in battling the pandemic, and how deep learning tools for medical imaging can help us improve the quality of COVID-19 testing.
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
Machine Learning How To Implement Support Vector Machine With Scikit-Learn In this tutorial we'll cover: An introduction to the support vector machine algorithmImplementing SVM using Python and SklearnSo, let's get started! Bring this project to life Run on gradientIntroduction to
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 Implementing The Levenshtein Distance for Word Autocompletion and Autocorrection The Levenshtein distance is a text similarity measure that compares two words and returns a numeric value representing the distance between them. The distance reflects the total number of single-character
