Convolutional Neural Networks in TensorFlow

所在平台: Coursera

课程主页: https://www.coursera.org/learn/convolutional-neural-networks-tensorflow

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课程简介

课程名称：在TensorFlow中使用卷积神经网络 课程概述：如果您是一名软件开发人员，希望构建可扩展的人工智能算法，那么您需要了解如何使用工具来构建这些算法。本课程是即将推出的TensorFlow机器学习专业的一部分，将教授您使用TensorFlow的最佳实践，TensorFlow是一个流行的开源机器学习框架。在deeplearning.ai TensorFlow专业的第二门课程中，您将学习提高计算机视觉模型的高级技术，探索如何处理不同形状和大小的真实世界图像，了解图像在卷积过程中的变化，以理解计算机如何“看”信息，绘制损失和准确率，探索防止过拟合的策略，包括数据增强和丢弃法。最后，本课程还将介绍迁移学习以及如何从模型中提取学习到的特征。 该机器学习和深度学习课程由Andrew Ng教授，讲授机器学习和深度学习最重要和基础的原则。这种新的deeplearning.ai TensorFlow专业教您如何使用TensorFlow来实现这些原则，以便您可以开始构建和应用可扩展的模型来解决实际问题。为了更深入了解神经网络的工作原理，我们建议您参加深度学习专业课程。 课程大纲： 第1部分：探索更大的数据集 您将在本专业的第一门课程中接触到TensorFlow，以及如何利用其高级API进行基本的图像分类，并了解卷积神经网络（ConvNets）。在本课程中，您将深入研究如何使用ConvNets处理真实世界的数据，学习提高ConvNet性能的技术，特别是在进行图像分类时！第一周，您将开始观察比以往使用的更大的数据集：猫和狗的数据集，这是一个经典的Kaggle图像分类挑战！ 第2部分：数据增强：避免过拟合的技术 您到目前为止已经听到了过拟合这个术语。过拟合是指在训练中过度专业化——即您的模型非常擅长分类其训练的内容，但不善于分类未见过的事物。为了更有效地使模型具有泛化能力，您当然需要更广泛的样本来进行训练。这并不总是可能，但图像增强可作为潜在的快捷方式，您将在本周学习到这一点。 第3部分：迁移学习 为自己构建模型是很棒且强大的。但正如您所见，您可能会受到手头数据的限制。并不是每个人都有访问庞大数据集的权限或者有效训练它们所需的计算能力。迁移学习可以帮助解决这个问题，部分经过在大型数据集上训练的模型的训练，您可以直接使用这些模型，或者您可以利用它们学习到的特征并将其应用于您的场景。本周您将深入探索迁移学习！ 第4部分：多类分类 您走了很长一段路，祝贺您！在我们从ConvNets转到下一个模块之前，还有一件事情要完成，那就是超越二元分类。到目前为止，您所有的示例都涉及到一种事物或另一种事物的分类——马或人，猫或狗。当您从二元分类转向分类时，需要考虑一些编码注意事项。本周您将关注这些内容！

课程大纲

Part: 1

Title:Exploring a Larger Dataset

Description:In the first course in this specialization, you had an introduction to TensorFlow, and how, with its high level APIs you could do basic image classification, and you learned a little bit about Convolutional Neural Networks (ConvNets). In this course you'll go deeper into using ConvNets will real-world data, and learn about techniques that you can use to improve your ConvNet performance, particularly when doing image classification!In Week 1, this week, you'll get started by looking at a much larger dataset than you've been using thus far: The Cats and Dogs dataset which had been a Kaggle Challenge in image classification!

Part: 2

Title:Augmentation: A technique to avoid overfitting

Description:You've heard the term overfitting a number of times to this point. Overfitting is simply the concept of being over specialized in training -- namely that your model is very good at classifying what it is trained for, but not so good at classifying things that it hasn't seen. In order to generalize your model more effectively, you will of course need a greater breadth of samples to train it on. That's not always possible, but a nice potential shortcut to this is Image Augmentation, where you tweak the training set to potentially increase the diversity of subjects it covers. You'll learn all about that this week!

Part: 3

Title:Transfer Learning

Description:Building models for yourself is great, and can be very powerful. But, as you've seen, you can be limited by the data you have on hand. Not everybody has access to massive datasets or the compute power that's needed to train them effectively. Transfer learning can help solve this -- where people with models trained on large datasets train them, so that you can either use them directly, or, you can use the features that they have learned and apply them to your scenario. This is Transfer learning, and you'll look into that this week!

Part: 4

Title:Multiclass Classifications

Description:You've come a long way, Congratulations! One more thing to do before we move off of ConvNets to the next module, and that's to go beyond binary classification. Each of the examples you've done so far involved classifying one thing or another -- horse or human, cat or dog. When moving beyond binary into Categorical classification there are some coding considerations you need to take into account. You'll look at them this week!

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