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
本课程是 Learning Deep Learning 专项课程 的一部分
位教师：Pearson
访问权限由 Coursera Learning Team 提供
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
- Prompt Engineering
- Image Analysis
- Generative AI
- Responsible AI
- Performance Tuning
- Convolutional Neural Networks
- LLM Application
- Deep Learning
- PyTorch (Machine Learning Library)
- Artificial Neural Networks
- Transfer Learning
- Large Language Modeling
- Vision Transformer (ViT)
- Model Deployment
- Tensorflow
- Multimodal Prompts
- Artificial Intelligence
- Computer Vision
- 技能部分已折叠。显示 9 项技能，共 18 项。
要了解的详细信息
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添加到您的领英档案
作业
5 项作业
授课语言：英语（English）
了解顶级公司的员工如何掌握热门技能
积累特定领域的专业知识
本课程是 Learning Deep Learning 专项课程 专项课程的一部分
在注册此课程时，您还会同时注册此专项课程。
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- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
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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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