Movies Recommendation Systems with TensorFlow
In this blog post, we cover the three types of recommender systems, and demo their use with the MovieLens dataset.
In this blog post, we cover the three types of recommender systems, and demo their use with the MovieLens dataset.
In this article, we explore the whys and the hows behind the fundamental process of pooling in CNN architectures, and then compare two common techniques: max and average pooling.
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.