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
Deep Learning Fine-Tuning Shallow Networks with Keras for Efficient Image Classification Neural networks with extensively deep architectures typically contain millions of parameters, making them both computationally expensive and time-consuming to train. In this tutorial, we'll achieve state-of-the-art image classification performance using
PyTorch A Comprehensive Guide to the DataLoader Class and Abstractions in PyTorch In this post, we'll deal with one of the most challenging problems in the fields of Machine Learning and Deep Learning: the struggle of loading and handling different types of data.
Computer Vision Object Detection Using Mask R-CNN with TensorFlow 2.0 and Keras In a previous tutorial, we saw how to use the open-source GitHub project Mask_RCNN with Keras and TensorFlow 1.14. In this tutorial, the project is inspected to replace
Object Detection Object Detection Using Mask R-CNN with TensorFlow 1.14 and Keras Mask R-CNN is an object detection model based on deep convolutional neural networks (CNN) developed by a group of Facebook AI researchers in 2017. The model can return both the
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
Tutorial How to Run TensorFlow Lite Models on Raspberry Pi The deep learning models created using TensorFlow require high processing capabilities to perform inference. Fortunately, there is a lite version of TensorFlow called TensorFlow Lite (TFLite for short) which allows
Interviews We shape our tools and thereafter our tools shape us: Interview with RunwayML founder Cristóbal Valenzuela Interview with Cristóbal Valenzuela founder of RunwayML, an electrifying new company that brings state-of-the-art machine learning techniques and architectures to media creatives with an intuitive and simple visual interface.
Announcement Paperspace Learn Hi everyone! We're excited to announce the Paperspace Learn, a completely free resource for anyone who wants to get into cloud computing or wants to learn about specialized topics such
Computer Vision How To Speed Up Object Detection Using NumPy Reshape and Transpose This is Part 4 of our ongoing series on NumPy optimization. In Parts 1 and 2 we covered the concepts of vectorization and broadcasting, and how they can be applied
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).
