Image Understanding with TensorFlow on GCP

开始时间: 08/08/2020 持续时间: Unknown

所在平台: Coursera

课程类别: 计算机科学

大学或机构: CourseraNew



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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

使用TensorFlow在GCP上进行图像理解:这是GCP专业化高级机器学习的第三门课程。在这个过程中 我们将研究使用卷积神经网络构建图像分类器的不同策略。我们将通过扩充,特征提取和微调超参数来提高模型的准确性,同时尝试避免过度拟合我们的数据。我们还将研究出现的实际问题,例如,当您没有足够的数据时,以及如何将最新的研究结果纳入我们的模型中。 您将在我们共同研究的实验室中,在各种公开数据集上进行动手实践,以建立和优化自己的图像分类模型。 先决条件:基本SQL,熟悉Python和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.





谷歌云平台 机器学习 高级机器学习 Tensorflow Python 图像理解 图像分类 图像分类器 图像分类模型 数据增强 特征提取