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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/cnns-and-rnns
课程评论：没有评论
课程名称：卷积神经网络（CNN）与递归神经网络（RNN）的基础 课程概述：本课程涵盖卷积神经网络（CNN）和递归神经网络（RNN）的基本概念，这两种网络广泛应用于计算机视觉和自然语言处理领域。在CNN部分，您将学习CNN的概念、两个主要操作（卷积和池化）以及CNN的结构。在RNN部分，您将学习RNN的概念、结构及其两个变体：长短期记忆网络（LSTM）和门控递归单元（GRU）。本课程的目标是使学习者对CNN和RNN有基本的理解。通过本课程，您将掌握进行计算机视觉和自然语言处理所需的技能。 课程大纲： - 第1周：CNN基础 - 第2周：卷积与池化 - 第3周：CNN的结构 - 第4周：递归神经网络 - 第5周：LSTM与GRU
Name:Week 1. CNN Basics
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Name:Week 2. Convolution and Pooling
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Name:Week 3. Structure of CNNs
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Name:Week 4. Recurrent Neural Network
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Name:Week5. LSTM GRU
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This course covers fundamental concepts of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which are widely used in computer vision and natural language processing areas. In the CNN part, you will learn the concepts of CNNs, the two major operators (convolution and pooling), and the structure of CNNs. In the RNN part, you will learn the concept and the structure of RNNs, and the two variants of RNNs, LSTMs and GRUs. The goal of this course is to give learners basic understanding of CNNs and RNNs. Throughout this course, you will be equipped with skills required for computer vision and natural language processing.
