This course covers key deep learning architectures such as BERT and GPT, focusing on their use in applications like chatbots and prompt tuning. You will learn how to build models that combine text and images, and generate text from visual data. The course also addresses multitask learning and computer vision tasks, including object detection and segmentation, using networks like R-CNN, U-Net, and Mask R-CNN. Topics include ethical considerations in AI and practical advice for tuning and deploying models. Through hands-on projects in TensorFlow and PyTorch, you will develop the skills needed to build, optimize, and apply deep learning solutions in real-world situations.
What you'll learn
understand key deep learning architectures
build models for text and image data
apply deep learning techniques for multitask learning
implement ethical considerations in AI
Course objectives
develop skills in TensorFlow and PyTorch
execute deep learning projects in real-world contexts
enhance understanding of model tuning and deployment