开始时间: 04/22/2022 持续时间: Unknown
所在平台: CourseraArchive 课程类别: 计算机科学 大学或机构: CourseraNew |
课程主页: https://www.coursera.org/archive/image-understanding-tensorflow-gcp
课程评论:没有评论
This is the third course of the Advanced Machine Learning on GCP specialization. In this course, We will take a look at different strategies for building an image classifier using convolutional neural networks. We'll improve the model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting our data. We will also look at practical issues that arise, for example, when you don’t have enough data and how to incorporate the latest research findings into our models. You will get hands-on practice building and optimizing your own image classification models on a variety of public datasets in the labs we’ll work on together. Prerequisites: Basic SQL, familiarity with Python and TensorFlow
We’ll start with a brief introduction where we’ll cover the image dataset you will be using for part of this course. Then we’ll tackle an image classification problem with a linear model in TensorFlow. After that we’ll move onto tackling the same problem using a Deep Neural Network. Lastly, we’ll close with a discussion and application of dropout which is a regularization technique for neural networks to help prevent them from memorizing our training dataset.
这是谷歌云平台高级机器学习专项课程系列第三课:通过TensorFlow进行图像理解。在这门课程中,首先将介绍使用卷积神经网络构建图像分类器的不同策略。其次将通过数据增强,特征提取和超参数调优来提高模型的准确性,同时避免过度拟合数据。学习过程中还将研究实际出现的问题,例如,当图像数据不足时如何处理问题以及如何将最新的研究成果纳入我们的模型。最后在这门课程的实践平台上,学员将在不同的公共数据集上构建和优化自己的图像分类器模型。先决条件:基本SQL,熟悉Python和TensorFlow。