Training complex deep learning models with large datasets takes along time. In this course, you will learn how to use accelerated GPU hardware to overcome the scalability problem in deep learning.

Training complex deep learning models with large datasets takes along time. In this course, you will learn how to use accelerated GPU hardware to overcome the scalability problem in deep learning.

Master the use of transfer learning to build a model that identifies stop signs. Learn how to use pre-trained models to detect traffic signs and apply transfer learning techniques for enhanced image classification accuracy.

This course will teach you how to identify, evaluate, and implement generative AI opportunities within your company to achieve strategic business goals.

This talk starts with demos of the basic standard transforms using the albumentations Python package, and work up to some more advanced strategies like CutMix and mixup.

Implement a neural network model for performing real-time image classification on a secured, internet-connected microcontroller-based device (Azure Sphere). Describe the components and steps for implementing a pre-trained image classification model on Azure Sphere.

Discover how to classify images using Hugging Face models for the crypto beans dataset. Learn to apply machine learning for image recognition and classification tasks in real-world applications.

Classify images by training a custom model with Azure AI Vision.

Learn to conduct image analysis using Keras with Python by constructing, training, and evaluating convolutional neural networks.

Learn how to create an image Q&A system using IBM watsonx and the multimodal Llama 3.2 model. Integrate computer vision and natural language processing to build an interactive AI-driven application.

Explore image recognition techniques using machine learning, focusing on practical implementations and algorithms.

Explore Image Representation and Processing to learn transforming images for AI and computer vision tasks efficiently.