pytorch PyTorch 101, Part 5: Understanding Hooks In this post, we cover debugging and Visualisation in PyTorch. We go over PyTorch hooks and how to use them to debug our backpass, visualise activations and modify gradients.
Tutorial PyTorch 101, Part 4: Memory Management and Using Multiple GPUs This article covers PyTorch's advanced GPU management features, including how to multiple GPU's for your network, whether be it data or model parallelism. We conclude with best practises for debugging memory error.
Tutorial PyTorch 101, Part 3: Going Deep with PyTorch In this tutorial, we dig deep into PyTorch's functionality and cover advanced tasks such as using different learning rates, learning rate policies and different weight initialisations etc
pytorch PyTorch 101, Part 2: Building Your First Neural Network In this part, we will implement a neural network to classify CIFAR-10 images. We cover implementing the neural network, data loading pipeline and a decaying learning rate schedule.
Deep Learning PyTorch 101, Part 1: Understanding Graphs, Automatic Differentiation and Autograd In this article, we dive into how PyTorch's Autograd engine performs automatic differentiation.
Tutorial Detecting and Localizing Pneumonia from Chest X-Ray Scans with PyTorch Over the years, we have seen very powerful models being built to distinguish between objects. These models keep getting better in terms of performance and latency day by day but have we ever
Deep Learning Going beyond torchvision models ResNets, DenseNets and Inception networks are undoubtedly some of the most powerful models out there for performing image classification and object recognition. These models have shown some promising results  in the ImageNet Large
Tutorial Physics control tasks with Deep Reinforcement Learning In this tutorial we will implement the paper Continuous Control with Deep Reinforcement Learning, published by Google DeepMind and presented as a conference paper at ICRL 2016. The networks will be implemented in
Series: Data Augmentation Data Augmentation for Bounding Boxes: Rethinking image transforms for object detection How to adapt major image augmentation techniques for object detection purposes. We also cover the implementation of horizontal flip augmentation.
Series: Data Augmentation Data Augmentation for Bounding Boxes: Scaling and Translation We implement Scale and Translate augmentation techniques, and what to do if a portion of your bounding box is outside the image after the augmentation.
Computer Vision Data Augmentation for Bounding Boxes: Rotation and Shearing This is part 3 of the series where we are looking at ways to adapt image augmentation techniques to object detection tasks. In this part, we will cover how to implement how to rotate and shear images as well as bounding boxes using OpenCV's affine transformation features.
Series: Data Augmentation Data Augmentation For Bounding Boxes: Building Input Pipelines for your detector Previously, we have covered a variety of image augmentation techniques such as Flipping, rotation, shearing, scaling and translating. This part is about how to bring it all together and bake it into the input pipeline for your deep network.
Series: Optimization Intro to optimization in deep learning: Busting the myth about batch normalization Batch Normalisation does NOT reduce internal covariate shift. This posts looks into why internal covariate shift is a problem and how batch normalisation is used to address it.
Series: Optimization Intro to optimization in deep learning: Vanishing gradients and choosing the right activation function An look into how various activation functions like ReLU, PReLU, RReLU and ELU are used to address the vanishing gradient problem, and how to chose one amongst them for your network.
Series: YOLO object detector in PyTorch How to implement a YOLO (v3) object detector from scratch in PyTorch: Part 1 Tutorial on building YOLO v3 detector from scratch detailing how to create the network architecture from a configuration file, load the weights and designing input/output pipelines.
Series: YOLO object detector in PyTorch How to implement a YOLO (v3) object detector from scratch in PyTorch: Part 2 Part 2 of the tutorial series on how to implement your own YOLO v3 object detector from scratch in PyTorch.
Series: YOLO object detector in PyTorch How to implement a YOLO (v3) object detector from scratch in PyTorch: Part 3 Part 3 of the tutorial series on how to implement a YOLO v3 object detector from scratch in PyTorch.
Series: YOLO object detector in PyTorch How to implement a YOLO (v3) object detector from scratch in PyTorch: Part 4 Part 4 of the tutorial series on how to implement a YOLO v3 object detector from scratch using PyTorch.
Series: YOLO object detector in PyTorch How to implement a YOLO (v3) object detector from scratch in PyTorch: Part 5 Part 5 of the tutorial series on how to implement a YOLO v3 object detector from scratch using PyTorch.
GPU Adversarial Autoencoders (with Pytorch) "Most of human and animal learning is unsupervised learning. If intelligence was a cake, unsupervised learning would be the cake [base], supervised learning would be the icing on the cake, and reinforcement
Windows Paperspace + RDP Microsoft's eponymous Remote Desktop Protocol (RDP) has been a mainstay in accessing remote desktops for decades and we're happy to announce that RDP is now integrated into the Paperspace platform. While not as
Advanced Bash on Windows 10 What is this Witchcraft? Ever since Satya Nadella's takeover, Microsoft has become much friendlier towards developers. Never has this transition been more apparent than the addition of the Bash shell to Windows 10.
Features New Feature: Advanced Settings Panel Starting today all Paperspace users can access an advanced menu and have even greater control over their streaming performance. Starting today there are two available settings (full-color and multi-monitor), and we intend to
Enterprise Paperspace Deployment Guide A full VDI implementation in the cloud Paperspace provides a complete virtual desktop solution in the cloud without any of the headaches of on-premise VDI. It’s easy to setup, simple to manage
