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
Learning Deep Learning: Unit 3
This course is part of Learning Deep Learning Specialization
Instructor: Pearson
Access provided by Flinders University
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
- Network Model
- Artificial Neural Networks
- Responsible AI
- LLM Application
- Prompt Engineering
- Computer Vision
- Large Language Modeling
- Artificial Intelligence
- PyTorch (Machine Learning Library)
- Tensorflow
- Performance Tuning
- Applied Machine Learning
- Image Analysis
- Deep Learning
- Application Deployment
- Generative AI
- Machine Learning Methods
- Multimodal Prompts
- Natural Language Processing
- Data Ethics
Details to know
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Assessments
5 assignments
Taught in English
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August 2025
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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.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
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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