Deep Learning

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 […]

Introduction 简介 In Lesson 2 we began our discussion of how the base in a convnet performs feature extraction. We learned about how the first two operations in this process occur in a Conv2D layer with relu activation. 在第 2 课中，我们开始讨论卷积网络中的基础如何执行特征提取。我们了解了此过程中的前两个操作如何在具有 relu 激活的 Conv2D 层中发生。 In this lesson, we’ll look at the third (and final) […]

This notebook is an exercise in the Computer Vision course. You can reference the tutorial at this link. Introduction 介绍 In this exercise, you’ll work on building some intuition around feature extraction. First, we’ll walk through the example we did in the tutorial again, but this time, with a kernel you choose yourself. We’ve mostly […]

import numpy as np from itertools import product def show_kernel(kernel, label=True, digits=None, text_size=28): # Format kernel kernel = np.array(kernel) if digits is not None: kernel = kernel.round(digits) # Plot kernel cmap = plt.get_cmap('Blues_r') plt.imshow(kernel, cmap=cmap) rows, cols = kernel.shape thresh = (kernel.max()+kernel.min())/2 # Optionally, add value labels if label: for i, j in product(range(rows), range(cols)): […]