In this blog, we show how to build an emoji suggestion system for short sentences rather than just a single word, and integrate it with a Flask interface.
In this tutorial, we walked through the capabilities and architecture of Open AI's Whisper, before showcasing two ways users can make full use of the model in just minutes with demos running in Gradient Notebooks and Deployments.
In this article, we compare and contrast various activation functions for deep learning with neural networks to try and determine the best class of these functions for different tasks.
In this article, we explore what global average and max pooling entail. We discuss why they have come to be used and how they measure up against one another. We also developed an intuition into why they work by performing a biopsy of our convnets and visualizing intermediate layers.
In this article, we demonstrate how to implement a version of a reinforcement learning technique Deep Q-Learning to create an AI agent capable of playing Checkers at a decent level.
This guide shows you how to setup the Stable Diffusion web UI in a Gradient Deployment, and get started synthesizing images in just moments with Gradient's powerful GPUs
In this article, we will define image segmentation, discover the right metrics to use in these tasks, build an end-to-end pipeline that can be used as a template for handling image segmentation problems, and talk about some useful applications of it.
In this blogpost we take a look at the three best GPU rendering softwares: Redshift, Octane, and Vray, and compare their functionalities for users to keep in mind when choosing their tools on Paperspace Core
In this deep dive of BERT, we explore the powerful NLP model's history, break down the approach and architecture behind the model, and take a look at some relevant experiments. We then close with a code demo showing how to use BERT, DistilBERT, RoBERTa, and ALBERT in a Gradient Notebook.
