This course covers the fundamentals of deep learning and its modern applications, including large language models and multimodal systems. It starts with an introduction to deep learning concepts, history, and necessary background. Students will learn the basics of neural networks through programming exercises, including how artificial neurons function, how networks are trained with algorithms such as backpropagation, and how to address issues like vanishing gradients and overfitting. The course then covers advanced topics such as convolutional neural networks for image classification, sequential models for language tasks, and building AI systems for translation, image captioning, and multitask learning. Students will gain practical experience using frameworks like TensorFlow and PyTorch. The course is suitable for those seeking to expand their knowledge and gain skills needed to build and deploy deep learning models.
Learning Deep Learning: Unit 1
您将学到什么
Grasp the core concepts and history of deep learning, including neural network fundamentals and training algorithms.
Develop hands-on skills in building, training, and evaluating neural networks using TensorFlow and PyTorch.
Apply advanced techniques to solve real-world problems in image classification, language processing, and multimodal AI.
Understand practical considerations and ethical aspects of deploying deep learning in real-world applications.
您将获得的技能
要了解的详细信息
可分享的证书
添加到您的领英档案
作业
3 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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