The course "Advanced Neural Network Techniques" delves into advanced neural network methodologies, offering learners an in-depth understanding of cutting-edge techniques such as Recurrent Neural Networks (RNNs), Autoencoders, Generative Neural Networks, and Deep Reinforcement Learning. Through hands-on projects and practical applications, learners will master the mathematical foundations and deployment strategies behind these models.
Advanced Neural Network Techniques
Advanced Neural Network Techniques
本课程是 Foundations of Neural Networks 专项课程 的一部分
位教师：Zerotti Woods
包含在 
中
您将学到什么
Analyze and implement Recurrent Neural Networks (RNNs) to process sequence data and solve tasks like time series prediction and language modeling.
Explore autoencoders for data compression, feature extraction, and anomaly detection, along with their applications in diverse fields.
Develop and evaluate generative models, such as GANs, understanding their mathematical foundations and deployment challenges.
Apply reinforcement learning techniques using Markov Chains and deep neural networks to tackle complex decision-making problems.
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
8 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 Foundations of Neural Networks 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。
- 向行业专家学习新概念
- 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
- 获得可共享的职业证书
该课程共有5个模块
This course explores advanced concepts and methodologies in neural networks, focusing on Recurrent Neural Networks (RNNs) and Autoencoders. You will analyze the core elements of these architectures, evaluate their applications across various domains, and propose innovative research directions. The curriculum also covers Generative Neural Networks, including their mathematical foundations and deployment constraints. Additionally, learners will gain hands-on experience in Reinforcement Learning, utilizing Markov Chains and Deep Neural Networks to solve complex problems. By the end of the course, you will be equipped with the skills to drive advancements in the field of neural networks.
涵盖的内容
2篇阅读材料
This module will discuss Recurrent Neural Networks. Students will explore the reasons for RNNS along with different techniques.
涵盖的内容
1个视频1篇阅读材料2个作业1个非评分实验室
This module will discuss Auto Encoders. Learners will explore the reasons for autoencoders along with different techniques and applications.
涵盖的内容
1个视频1篇阅读材料2个作业
This module will discuss Generative Deep Learning Models. You will study two particular models and go through examples of where they have been successfully deployed.
涵盖的内容
1个视频1篇阅读材料2个作业
This module will introduce reinforcement learning. We will discuss Markov Chains, Q-learning, and Deep Q-learning.
涵盖的内容
4个视频1篇阅读材料2个作业
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