2021 was doubtlessly an interesting year for most of our readers. Between the massive effect of the pandemic on daily lives and the consistent rollout of industry defining new papers and projects, machine learning scientists and enthusiasts have certainly had something going on. To help you remember all these exciting new happenings in the industry, we've collated the most popular and most useful blog posts from the Paperspace blog in 2021 in one place!
This series gives an advanced guide to different recurrent neural networks (RNNs). It will give readers an understanding of the networks themselves, their architectures, their applications, and how to bring the models to life using Keras.
This guide shows you how to set up the code for, set up a dataset for YOLO with annotation conversions, setting up training for, and inference of objects using YOLO v5
The awesome starter Workflow from Gradient that helps you build a web application that can turn a selfie into a deepfaked video of singing an Italian opera song. This tutorial introduces the new starter Workflow, and allows any Pro user to launch a fascinating first order motion model.
In this article, Ayoosh covered the basic building blocks of Open AI Gym. This includes environments, spaces, wrappers, and vectorized environments. Read this article and its follow up (see below) to rapidly boost your productivity with OpenAI Gym.
This post covers how to implement a custom environment in OpenAI Gym. As an example, you will see how to implement a custom environment that involves flying a Chopper (or a helicopter) while avoiding obstacles mid-air. Part 2 of Getting started with OpenAI Gym.
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This tutorial contains an overview of the Mask_RCNN Project. Follow this guide to learn about object Detection with TensorFlow 1.14, building the Mask R-CNN Model Architecture, training Mask R-CNN in TensorFlow 1.14, and using it for detecting Objects.
Interested in getting started with Deep Learning? This guide shows you how to get started with PyTorch, tensors, and constructing Neural Networks with PyTorch.
Follow this tutorial to see how to use the incredible GFP-GAN algorithm to upscale and restore the quality of damaged photos.
In this followup to the introduction to restoring photos with GFP-GAN, we look at using Gradient Deployments to create a Flask API making full use of the model.
In this blog, we showed an example of how to train and generalize Scaled-YOLOv4 on your custom dataset to detect custom objects.
In this article, we showed how to run Gradient Workflows with GPT-2 to generate novel text.
In this series, we covered most of the essential theory and concepts related to transfer learning. We learned about convolutional neural networks, how they're used with transfer learning, and gained an understanding of fine-tuning these models.
In this article, we looked at how you can use the Hugging Face package for sentiment analysis, question and answering, named-entity recognition, summarization, translation, and tokenization.
A guide to using Gradient Notebooks to generate gorgeous pixel artwork using the PixRay library suite.
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