VOOZH about

URL: https://www.coursera.org/learn/pearson-learning-deep-learning-from-perception-to-large-language-models-vi-zc8ia

⇱ Learning Deep Learning: Unit 3 | Coursera

Learning Deep Learning: Unit 3

Keep adding new skills with 10,000+ programs for $239 (usually $399). Save now.

Learning Deep Learning: Unit 3

Included with

Ask Coursera

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

6 hours to complete
Flexible schedule
Learn at your own pace
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.

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

5 assignments

Taught in English

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

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructors

Pearson
268 Courses•65,339 learners

Explore more from Machine Learning

Why people choose Coursera for their career

👁 Image

Felipe M.

Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
👁 Image

Jennifer J.

Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
👁 Image

Larry W.

Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
👁 Image

Chaitanya A.

"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

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.

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

This course is currently available only to learners who have paid or received financial aid, when available.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,