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)): […]

This notebook is an exercise in the Computer Vision course. You can reference the tutorial at this link. Accelerate Training with a Kaggle GPU! Did you know Kaggle offers free time with a GPU accelerator? You can speed up training neural networks in this course by switching to GPU in the Accelerator option on the […]

Welcome to Computer Vision! 欢迎来到计算机视觉！ Have you ever wanted to teach a computer to see? In this course, that’s exactly what you’ll do! 你有没有想过教计算机看东西？ 在本课程中，这正是您要做的！ In this course, you’ll: 在本课程中，您将： Use modern deep-learning networks to build an image classifier with Keras 使用现代深度学习网络通过 Keras 构建图像分类器 Design your own custom convnet with reusable blocks 使用可重复使用的块设计您自己的自定义卷积网络 Learn the […]

This notebook is an exercise in the Intro to Deep Learning course. You can reference the tutorial at this link. Introduction 介绍 In this exercise, you’ll build a model to predict hotel cancellations with a binary classifier. 在本练习中，您将构建一个模型来使用二元分类器预测酒店取消情况。 # Setup plotting import matplotlib.pyplot as plt plt.style.use('seaborn-v0_8-whitegrid') # Set Matplotlib defaults plt.rc('figure', autolayout=True) plt.rc('axes', labelweight='bold', labelsize='large', […]

Introduction 介绍 So far in this course, we’ve learned about how neural networks can solve regression problems. Now we’re going to apply neural networks to another common machine learning problem: classification. Most everything we’ve learned up until now still applies. The main difference is in the loss function we use and in what kind of […]

This notebook is an exercise in the Intro to Deep Learning course. You can reference the tutorial at this link. Introduction 介绍 In this exercise, you’ll add dropout to the Spotify model from Exercise 4 and see how batch normalization can let you successfully train models on difficult datasets. 在本练习中，您将向练习 4 中的 Spotify 模型添加 dropout，并了解批量归一化如何让您在困难的数据集上成功训练模型。 […]