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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/generative-deep-learning-with-tensorflow
课程评论：没有评论
课程名称：使用TensorFlow的生成深度学习 概述：本课程将带您学习生成深度学习的多种技术，包括： 1. **神经风格迁移**：学习如何提取图像的内容（例如天鹅）和绘画的风格（例如立体主义或印象派），并将内容与风格结合生成新图像。 2. **自编码器**（AutoEncoders）：在熟悉的MNIST数据集上构建简单的自编码器，并在Fashion MNIST数据集上掌握更复杂的深度和卷积架构，理解DNN与CNN自编码器模型的结果差异，探讨去噪方法，使用TensorFlow构建CNN自编码器从噪声图像输出清晰图像。 3. **变分自编码器**（Variational AutoEncoders, VAEs）：探索VAEs生成全新数据，进行动漫脸部生成并与参考图像进行比较。 4. **生成对抗网络**（GANs）：了解GAN的发明、特性及架构，理解其与VAEs的区别，学习生成器和判别器的功能，掌握模型的两个训练阶段和引入噪声的角色，最终构建能够生成面孔的GAN。 该课程是DeepLearning.AI TensorFlow：高级技术专项的一部分，旨在向学习者介绍TensorFlow的高级功能，让他们在构建和训练先进的机器学习模型时拥有更高的控制权。 目标人群：本专项适合拥有TensorFlow基础知识的早期和中级软件工程师和机器学习工程师，希望通过学习高级TensorFlow特性拓展知识和技能。 课程大纲： 1. **第一周：风格迁移** - 学习神经风格迁移的基本概念与实现方法。 2. **第二周：自编码器** - 掌握自编码器的构建与应用，学习去噪技巧。 3. **第三周：变分自编码器** - 生成新数据并进行动漫脸部图像生成。 4. **第四周：生成对抗网络** - 深入理解GAN，并构建自己的面孔生成模型。 这个课程将帮助您掌握生成深度学习的核心技术，提升您在机器学习领域的能力。
Part: 1
Title:Week 1: Style Transfer
Description: This week, you will learn how to extract the content of an image (such as a swan), and the style of a painting (such as cubist, or impressionist), and combine the content and style into a new image. This is called neural style transfer, and you'll learn how to extract these kinds of features using transfer learning.
Part: 2
Title:Week 2: AutoEncoders
Description:This week, you’ll get an overview of AutoEncoders and how to build them with TensorFlow. You'll learn how to build a simple AutoEncoder on the familiar MNIST dataset, before diving into more complicated deep and convolutional architectures that you'll build on the Fashion MNIST dataset. You'll get to see the difference in results of the DNN and CNN AutoEncoder models, and then identify ways to denoise noisy images. You'll finish the week building a CNN AutoEncoder using TensorFlow to output a clean image from a noisy one!
Part: 3
Title:Week 3: Variational AutoEncoders
Description:This week you will explore Variational AutoEncoders (VAEs) to generate entirely new data. In this week’s assignment, you will generate anime faces and compare them against reference images.
Part: 4
Title:Week 4: GANs
Description:This week, you’ll learn about GANs. You'll learn what they are, who invented them, their architecture and how they vary from VAEs. You'll get to see the function of the generator and the discriminator within the model, and the concept of 2 training phases and the role of introduced noise. Then you'll end the week building your own GAN that can generate faces! How cool is that!
In this course, you will: a) Learn neural style transfer using transfer learning: extract the content of an image (eg. swan), and the style of a painting (eg. cubist or impressionist), and combine the content and style into a new image. b) Build simple AutoEncoders on the familiar MNIST dataset, and more complex deep and convolutional architectures on the Fashion MNIST dataset, understand the difference in results of the DNN and CNN AutoEncoder models, identify ways to de-noise noisy images, and build a CNN AutoEncoder using TensorFlow to output a clean image from a noisy one. c) Explore Variational AutoEncoders (VAEs) to generate entirely new data, and generate anime faces to compare them against reference images. d) Learn about GANs; their invention, properties, architecture, and how they vary from VAEs, understand the function of the generator and the discriminator within the model, the concept of 2 training phases and the role of introduced noise, and build your own GAN that can generate faces. The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture, and gives them the tools to create and train advanced ML models. This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.
