Object Detection Object Detection with PyTorch and Detectron2 In this post, we will show you how to train Detectron2 on Gradient to detect custom objects ie Flowers on Gradient. We will show you how to label custom dataset
Gradient Spectrum Labs Collaborates with Paperspace to Boost NLP-Based Toxic Chat Modeling Efforts Spectrum Labs is known for its highly accurate, tunable context-aware models and its workflow tool, Guardian. Spectrum Labs is growing its machine learning team and its customer base rapidly.
Interviews We are the Music Makers and We are the Dreamers of Dreams - Art & Technology with Dan Jeffries Interview with Dan Jeffries -- a prolific writer, blogger, teacher, consultant, and futurist in addition to his day job as the Chief Technology Evangelist with our friends over at Pachyderm.
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
Optimization Gradient Descent and Optimization In Deep Learning The most common method underlying many of the deep learning model training pipelines is gradient descent. But vanilla gradient descent can encounter several problems, like getting stuck at local minima
Tutorial How To Implement Text Recommendation on Android Using the Levenshtein Distance Typing text on your phone might not be as comfortable as writing on your PC. That's why most applications that take text as an input offer users suggested corrections and
Tools ML Platforms: Buy vs Build If you follow the emerging discipline of MLOps, by now you've probably heard of some of the well-known internally developed ML platforms like Uber's Michelangelo and AirBnB's BigHead. The big
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
Gradient Autoscaling now available for model serving and deployments We're pleased to announce the release of autoscaling for model serving and deployments on Paperspace Gradient. Autoscaling is an essential MLOps tool for adjusting compute resources or instances dedicated to
Computer Vision Efficient Channel Attention for Deep Convolutional Neural Networks (ECA-Net) In this article we'll dive into an in-depth discussion of a recently proposed attention mechanism, namely ECA-Net, published at CVPR 2020.
Announcement Introducing ML News 📈 ML News (MLN for short) is a community for sharing and discussing all things related to machine learning, deep learning, AI, data science, and the like.
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).
Machine Learning Working with Different Genetic Algorithm Representations in Python Depending on the nature of the problem being optimized, the genetic algorithm (GA) supports two different gene representations: binary, and decimal. The binary GA has only two values for its
New: Better secrets management arrives in Paperspace Gradient We're delighted to announce the arrival of new secrets management in Gradient! Secrets are now available in Gradient Projects!Secrets are a way to scope keys and credentials locally at
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.
Customer Story An Evolution in Gaming Interview with Wessel Stoop, the creator of The Sapling: a game about evolution for Windows and Linux.
Paperspace Joins NVIDIA Cloud Service Provider Program, Accelerates Cloud GPU Adoption & Remote Work New York City, September 10, 2020 - Paperspace today announced it has joined the Cloud Service Provider Program within the NVIDIA Partner Network (NPN) to bring GPU acceleration to the
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
New Fast.ai course for 2020 We first partnered with Jeremy Howard and the Fast.ai team around three years ago for the launch of the original course. If you aren't already familiar, Fast.ai is
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
Keras A Guide to TensorFlow Callbacks TensorFlow callbacks are an essential part of training deep learning models, providing a high degree of control over many aspects of your model training.
Gradient Paperspace CEO Dillon Erb joins the TWIML AI Podcast Few people have as much expertise in the machine learning space as Sam Charrington from TWIML (This Week in Machine Learning). For years now he's been inspiring us with his
Deepfakes Getting Real with Deepfake Artist Jesse Richards Interview with Tiktok and Instagram deepfaker, Jesse Richards (@Iamadeepfaker).
Keras How to Build a Variational Autoencoder in Keras This tutorial gives an introduction to the variational autoencoder (VAE) neural network, how it differs from typical autoencoders, and its benefits. We'll then build a VAE in Keras that can
