Just For My Daily Diary
Just For My Daily Diary
This notebook is an exercise in the AI Ethics course. You can reference the tutorial at this link. In the tutorial, you learned about human-centered design (HCD) and became familiar with six general steps to apply it to AI systems. In this exercise, you will identify and address design issues in six interesting AI use […]
Introduction 简介 Before selecting data and training models, it is important to carefully consider the human needs an AI system should serve – and if it should be built at all. 在选择数据和训练模型之前，重要的是要仔细考虑 AI 系统应该满足的人类需求 – 以及是否应该构建它。 Human-centered design (HCD) is an approach to designing systems that serve people’s needs. 以人为本的设计 (HCD) 是一种设计满足人们需求的系统的方法。 In this tutorial, […]
Welcome to the AI Ethics course! 欢迎来到 AI 伦理课程！ AI increasingly has an impact on everything from social media to healthcare. AI is used to make credit card decisions, to conduct video surveillance in airports, and to inform military operations. These technologies have the potential to harm or help the people that they serve. By […]
This notebook is an exercise in the Computer Vision course. You can reference the tutorial at this link. Introduction 简介 In these exercises, you’ll explore what effect various random transformations have on an image, consider what kind of augmentation might be appropriate on a given dataset, and then use data augmentation with the Car or […]
Introduction 简介 Now that you’ve learned the fundamentals of convolutional classifiers, you’re ready to move on to more advanced topics. 现在您已经了解了卷积分类器的基础知识，可以继续学习更高级的主题了。 In this lesson, you’ll learn a trick that can give a boost to your image classifiers: it’s called data augmentation. 在本课中，您将学习一种可以增强图像分类器的技巧：它被称为数据增强。 The Usefulness of Fake Data 虚假数据的用处 The best way to improve the performance […]
This notebook is an exercise in the Computer Vision course. You can reference the tutorial at this link. Introduction 介绍 In these exercises, you’ll build a custom convnet with performance competitive to the VGG16 model from Lesson 1. 在这些练习中，您将构建一个自定义卷积网络，其性能可与第 1 课中的 VGG16 模型相媲美。 Get started by running the code cell below. 通过运行下面的代码单元开始。 # Setup feedback […]
Introduction 介绍 Now that you’ve seen the layers a convnet uses to extract features, it’s time to put them together and build a network of your own! 现在您已经了解了卷积网络用于提取特征的各层，是时候将它们组合在一起并构建自己的网络了！ Simple to Refined 从简单到精细 In the last three lessons, we saw how convolutional networks perform feature extraction through three operations: filter, detect, and condense. A single round […]
This notebook is an exercise in the Computer Vision course. You can reference the tutorial at this link. Introduction 简介 In these exercises, you’ll explore the operations a couple of popular convnet architectures use for feature extraction, learn about how convnets can capture large-scale visual features through stacking layers, and finally see how convolution can […]
import numpy as np from itertools import product from skimage import draw, transform def circle(size, val=None, r_shrink=0): circle = np.zeros([size[0]+1, size[1]+1]) rr, cc = draw.circle_perimeter( size[0]//2, size[1]//2, radius=size[0]//2 – r_shrink, shape=[size[0]+1, size[1]+1], ) if val is None: circle[rr, cc] = np.random.uniform(size=circle.shape)[rr, cc] else: circle[rr, cc] = val circle = transform.resize(circle, size, order=0) return circle def […]
This notebook is an exercise in the Computer Vision course. You can reference the tutorial at this link. Introduction 介绍 In these exercises, you’ll conclude the feature extraction begun in Exercise 2, explore how invariance is created by maximum pooling, and then look at a different kind of pooling: average pooling. 在这些练习中，您将完成练习 2 中开始的特征提取，探索最大池化如何产生不变性，然后研究另一种池化：平均池化。 Run […]