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
Python NumPy Array Processing With Cython: 1250x Faster This tutorial will show you how to speed up the processing of NumPy arrays using Cython. By explicitly specifying the data types of variables in Python, Cython can give drastic
Generative Models Implementing CycleGAN for Age Conversion If you've been on any sort of social media this year, you've probably seen people uploading a recent picture of themselves right next to another picture of what they'll look
Python Boosting Python Scripts With Cython Python might be one of today's most popular programming languages, but it's definitely not the most efficient. In the machine learning world in particular, practitioners sacrifice efficiency for the ease-of-use
Deep Learning Why PyTorch Is the Deep Learning Framework of the Future Are you looking for an efficient and modern framework to create your deep learning model? Look no further than PyTorch! In this article we'll cover an introduction to PyTorch, what
Series: Gradient Descent with Python Implementing Gradient Descent in Python, Part 4: Applying to Any Number of Neurons In this tutorial we extend our implementation of gradient descent to work with a single hidden layer with any number of neurons. Part 4 is divided into two sections. In
Gradient Introducing Gradient Public Profiles 📣 Public profiles in Gradient are designed to make it easier to publish and discover machine learning projects on the Gradient platform. As always, we are eager to hear your feedback.
Announcement Introducing Gradient Community Notebooks: Easily Run ML Notebooks on Free GPUs Whether you’re a machine learning enthusiast, researcher, or professional, setting up and managing your work environment can be a complex and distracting process. Even when you're done dealing with
Interviews DeepFake Faceoff w/ Dr. Fakenstein & CtrlShiftFace Deepfake technology is unlocking a new era of media production. As with all technologies, both positive and harmful use cases exist. As ethical technologists, we aspire to push the limits
Announcement Introducing the next iteration of Gradient: Run on-prem, in your cloud, or hybrid Update: This launch was featured in TechCrunch Over the last few years, we've fielded countless requests to run Gradient, our MLOps SaaS platform, on existing infrastructure, ranging from local bare
Tutorial Neural Machine Translation with TensorFlow If you are a fan of Google translate or some other translation service, do you ever wonder how these programs are able to make spot-on translations from one language to
Series: Gradient Descent with Python Implementing Gradient Descent in Python, Part 3: Adding a Hidden Layer Hello again in the series of tutorials for implementing a generic gradient descent (GD) algorithm in Python for optimizing parameters of artificial neural network (ANN) in the backpropagation phase. The
Tutorial Unpaired Image to Image Translation with CycleGAN Yann LeCun, director of AI research at Facebook and Professor at NYU described Generative Adversarial Networks, GANs as the most interesting idea in Machine Learning in the last 10 years.
Gradient New Gradient Python SDK Build out complex end-to-end machine learning pipelines with the new Gradient Python SDK.
Series: Gradient Descent with Python Implementing Gradient Descent in Python, Part 2: Extending for Any Number of Inputs Hello again in the series of tutorials for implementing a generic gradient descent (GD) algorithm in Python for optimizing parameters of artificial neural network (ANN) in the backpropagation phase. The
Tutorial TensorFlow 2.0 in Action TensorFlow is one of the most popular frameworks used for deep learning projects and is approaching a major new release- TensorFlow 2.0. Luckily, we don't have to wait for
Advanced Technologies Group Move Quickly, Think Deeply: How Research Is Done @ Paperspace ATG The Advanced Technologies Group is an R&D-focused team here at Paperspace, comprising ML Engineers and Researchers. As a group, we're interested in exploring advanced topics in deep learning,
Series: Gradient Descent with Python Implementing Gradient Descent in Python, Part 1: The Forward and Backward Pass Through a series of tutorials, the gradient descent (GD) algorithm will be implemented from scratch in Python for optimizing parameters of artificial neural network (ANN) in the backpropagation phase. The
Deep Learning 6 Interesting Deep Learning Applications for NLP Read on to discover deep learning methods are being applied in the field of natural language processing, achieving state-of-the-art results for most language problems.
Deep Learning Building a State of the Art Bacterial Classifier with Paperspace Gradient and Fast.ai One of the great promises of Deep Learning is its applicability in a wide variety of complex tasks. Recent years have seen an explosion in the number of fields Deep
Announcement Train ML Models on Free Cloud GPUs ⚡ When we started Paperspace back in 2014, our mission was to make cloud GPU resources more accessible and less expensive for everyone. Since inception, we have continued to offer a
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
