Expert Parallelism: Scaling Mixture-of-Experts Models
Learn how Expert Parallelism boosts Mixture-of-Experts model efficiency and GPU scalability for faster, more optimized large-scale deep learning training.
Hierarchical Reasoning Model Explained
Hierarchical Reasoning Model (HRM) brings a brain-inspired approach to AI reasoning. HRMs reduce memory usage while improving efficiency.
Building sharp regression models with K-Means Clustering + SVR
Discover how combining K-Means clustering with SVR improves regression accuracy, especially for complex or unevenly distributed datasets.
Multi-GPU on raw PyTorch with Hugging Face’s Accelerate library
In this article, we examine HuggingFace’s Accelerate library for multi-GPU deep learning. We apply Accelerate with PyTorch and show how it can be used to simplify transforming raw PyTorch into code that can be run on a distributed machine system.
PyTorch Loss Functions
Follow this guide to learn about the various loss functions available to use with PyTorch.
Intro to Optimization in Deep Learning: Vanishing Gradients and Choosing the Right Activation Function
An look into how various activation functions like ReLU, PReLU, RReLU and ELU are used to address the vanishing gradient problem, and how to chose one amongst them for your network.
Attention Mechanisms in Computer Vision: CBAM
This post covers an in-depth analysis of the convolution block attention module (CBAM).
Building a Custom Real-time Chat Experience with the cloud provider’s Gradient Platform
Learn how to build a custom chat application that mimics the ChatGPT experience using the cloud provider’s Gradient Platform.
Introduction to Extractive and Abstractive Summarization Techniques
In this theory we cover the background theory behind a variety of methodologies for abstractive text summarization
A Deeper Dive into How CNNs Interpret Images.
In this article, we’ll explore how a CNN views and comprehends images without diving into the mathematical intricacies.
