AI

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。 […]

Introduction 介绍 There’s more to the world of deep learning than just dense layers. There are dozens of kinds of layers you might add to a model. (Try browsing through the Keras docs for a sample!) Some are like dense layers and define connections between neurons, and others can do preprocessing or transformations of other […]

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 learn how to improve training outcomes by including an early stopping callback to prevent overfitting. 在本练习中，您将学习如何通过添加提前停止回调以防止过度拟合来改进训练结果。 When you’re ready, run this next cell to set everything up! 准备好后，运行下一个单元格来设置一切！ # […]

Introduction 介绍 Recall from the example in the previous lesson that Keras will keep a history of the training and validation loss over the epochs that it is training the model. In this lesson, we’re going to learn how to interpret these learning curves and how we can use them to guide model development. In […]