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Learning Deep Learning: Unit 3

Learning Deep Learning: Unit 3

This course is part of Learning Deep Learning Specialization

Instructors: Pearson

Included with

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1 module

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

6 hours to complete

Flexible schedule

Learn at your own pace

1 module

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

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

5 assignments

Taught in English

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Build your subject-matter expertise

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 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.

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

33 videos Total 239 minutes

Topics 1 minute
Overview of BERT 10 minutes
Overview of GPT 7 minutes
From GPT to GPT4 17 minutes
Handling Chat History 6 minutes
Prompt Tuning 9 minutes
Retrieving Data and Using Tools 8 minutes
Open Datasets and Models 6 minutes
Demo: Large Language Model Prompting 6 minutes
Lesson 8 Summary 2 minutes
Topics 1 minute
Multimodal learning 8 minutes
Programming Example: Multimodal Classification with TensorFlow 8 minutes
Programming Example: Multimodal Classification with PyTorch 8 minutes
Image Captioning with Attention 6 minutes
Programming Example: Image Captioning with TensorFlow 18 minutes
Programming Example: Image Captioning with PyTorch 17 minutes
Multimodal Large Language Models 20 minutes
Lesson 9 Summary 2 minutes
Topics 1 minute
Multitask Learning 6 minutes
Programming Example: Multitask Learning with TensorFlow 5 minutes
Programming Example: Multitask Learning with PyTorch 6 minutes
Object Detection with R-CNN 7 minutes
Improved Object Detection with Fast and Faster R-CNN 5 minutes
Segmentation with Deconvolution Network and U-Net 8 minutes
Instance Segmentation with Mask R-CNN 3 minutes
Lesson 10 Summary 2 minutes
Topics 1 minute
Ethical AI and Data Ethics 19 minutes
Process for Tuning a Network 6 minutes
Further Studies 5 minutes
Learning Deep Learning: Summary 8 minutes

5 assignments Total 150 minutes

Large Language Models Quiz 30 minutes
Multi-Modal Networks and Image Captioning Quiz 30 minutes
Multi-Task Learning and Computer Vision Beyond Classification Quiz 30 minutes
Applying Deep Learning Quiz 30 minutes
End of Course Assessment 30 minutes

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Instructors

Pearson

268 Courses 48,983 learners

Offered by

Pearson

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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.