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.
Learning Deep Learning: Unit 3
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Learning Deep Learning: Unit 3
This course is part of Learning Deep Learning Specialization
Instructor: Pearson
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Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
6 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Master large language models and transformer architectures for advanced natural language processing applications.
Build and deploy multimodal networks that integrate multiple data types, such as text and images.
Implement multitask learning and solve advanced computer vision problems, including object detection and segmentation.
Apply ethical principles and practical strategies for tuning and deploying deep learning models in real-world settings.
Skills you'll gain
Details to know
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Recently updated!
August 2025
Assessments
5 assignments
Taught in English
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This course is part of the Learning Deep Learning Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There is 1 module in this course
This module explores advanced deep learning topics, including large language models (LLMs) and their transformer architectures, multimodal networks that integrate multiple data types, and multitask learning for complex computer vision tasks like object detection and segmentation. Practical implementation is demonstrated using TensorFlow and PyTorch. The module concludes with guidance on ethical considerations, model tuning, and further learning directions, equipping learners to responsibly apply deep learning in real-world scenarios.
What's included
33 videos5 assignments
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Frequently asked questions
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.
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