Checkpointing in TensorFlow
Follow this guide to learn how to directly monitor and checkpoint your models during the training process!
Deep Learning Architecture Review: DenseNet, ResNeXt, MnasNet & ShuffleNet v2
Understand the strengths and applications of popular deep learning architectures—DenseNet, ResNeXt, MnasNet, and ShuffleNet v2. Learn how these models enhance efficiency, accuracy, and performance in AI and computer vision tasks.
Constructing Neural Networks From Scratch: Part 1
Learn how to construct neural networks from scratch with NumPy, and simultaneously see how the internal mechanisms behind popular libraries like PyTorch and Keras are implemented.
Deep Learning Architectures Explained: ResNet, InceptionV3, SqueezeNet
Understand the basics of ResNet, InceptionV3, and SqueezeNet architecture and how they power deep learning models. Learn their architectures, key features, and how they improve accuracy and efficiency.
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.
