Energy-Efficient Deep Learning — How Precision Scaling Reduces Carbon Footprint
Learn how precision scaling lowers compute needs, cuts energy use, and reduces the carbon footprint of deep learning models for sustainable AI.
PyTorch Loss Functions
Follow this guide to learn about the various loss functions available to use with PyTorch.
Face Generation with GANs
A primer for developing a custom neural network to learn to generate novel facial images using Deep Convolutional generative adversarial networks.
How to Train YOLO v5 on a Custom Dataset | the cloud provider
Learn how to train YOLOv5 on a custom dataset with this step-by-step guide. Discover data preparation, model training, hyperparameter tuning, and best practices for object detection.
Faster R-CNN Explained for Object Detection Tasks
Learn how Faster R-CNN works for object detection tasks with its region proposal network and end-to-end architecture.
Understanding LLM Poisoning
Discover how LLM poisoning works, why even 0.01% poisoned data can compromise AI systems, and the steps to prevent backdoor attacks in models.
Understanding Feedforward and Feedback Networks (or recurrent) neural network
Explore the key differences between feedforward and feedback neural networks, how they work, and where each type is best applied in AI and machine learning.
U-Net Architecture For Image Segmentation
Image segmentation makes it easier to work with computer vision applications. We look at U-Net, a convolutional neural network.
Understanding Filters in Convolutional Neural Networks (CNNs)
Explore techniques for filtering image data and learn what these filters do to an image as it passes through the layers of a Convolutional Neural Network
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.
