Computer Vision Image Captioning With AI One application that has really caught the attention of many folks in the space of artificial intelligence is image captioning. If you think about it, there is seemingly no way
Series: GauGAN Understanding GauGAN Part 2: Training on Custom Datasets In Part One, we covered the basic components of GauGAN as well as the loss functions it makes use of. In this part, we'll cover the training details and see
Series: GauGAN Understanding GauGAN Part 1: Unraveling Nvidia's Landscape Painting GANs One of the most interesting papers presented at CVPR in 2019 was Nvidia's Semantic Image Synthesis with Spatially-Adaptive Normalization. This features their new algorithm, GauGAN, which can effectively turn doodles
Generative Models A 2020 Guide to Synthetic Media Media synthesis is an exciting new area of research that has seen great advancements in the past few years. This field has the potential to completely revolutionize the way we
Machine Learning Introduction to Bagging and Ensemble Methods The bias-variance trade-off is a challenge we all face while training machine learning algorithms. Bagging is a powerful ensemble method which helps to reduce variance, and by extension, prevent overfitting.
Artificial Intelligence Introducing The AI Wiki 🧠 We made the AI Wiki to keep the world of artificial intelligence a little less daunting. With easy-to-understand definitions and helpful graphics, the AI Wiki explains dozens of topics in AI, machine learning and deep learning, for beginners and practitioners alike.
Machine Learning Implementing Gradient Boosting in Python Are you working on a regression problem and looking for an efficient algorithm to solve your problem? If yes, you must explore gradient boosting regression (or GBR). In this article
Announcement New Feature: YAML Config File on Gradient The YAML config file simplifies your work from the command line by storing default arguments for different tasks.
Python Cythonizing Genetic Algorithms: 18x Faster In this tutorial we use Cython to reduce the execution time of the genetic algorithm implemented in Python. We've brought down our computational time from 1.46 seconds to a mere 0.08 seconds, so that 1 million generations run in less than 10 seconds with Cython, compared to 180 seconds in Python.
Computer Vision Building Image Datasets for Computer Vision Algorithms In my experience, data is one of the most challenging issues that we face in developing any ML/AI pipelines in the field of computer vision. There are two aspects
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 Update! (December 2019) CtrlShiftFace has a new video Home Stallone produced with Paperspace: Deepfake technology is unlocking a new era of media production. As with all technologies, both positive and
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.
