中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
您将学到什么
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.
您将获得的技能
- Computer Vision
- Artificial Neural Networks
- Multimodal Prompts
- Large Language Modeling
- Network Model
- Artificial Intelligence
- Tensorflow
- Generative AI
- Data Ethics
- Performance Tuning
- Natural Language Processing
- Machine Learning Methods
- Responsible AI
- LLM Application
- Deep Learning
- PyTorch (Machine Learning Library)
- Prompt Engineering
- Image Analysis
- Applied Machine Learning
- Application Deployment
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最近已更新！
August 2025
作业
5 项作业
授课语言：英语（English）
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本课程是 Learning Deep Learning 专项课程 专项课程的一部分
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该课程共有1个模块
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.
涵盖的内容
33个视频5个作业
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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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