Year: 2024

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 […]

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 train a neural network on the Fuel Economy dataset and then explore the effect of the learning rate and batch size on SGD. 在本练习中，您将在 Fuel Economy 数据集上训练神经网络，然后探索学习率和批量大小对 SGD 的影响。 […]

Introduction 介绍 In the first two lessons, we learned how to build fully-connected networks out of stacks of dense layers. When first created, all of the network’s weights are set randomly — the network doesn’t "know" anything yet. In this lesson we’re going to see how to train a neural network; we’re going to see […]

This notebook is an exercise in the Intro to Deep Learning course. You can reference the tutorial at this link. Introduction 介绍 In the tutorial, we saw how to build deep neural networks by stacking layers inside a Sequential model. By adding an activation function after the hidden layers, we gave the network the ability […]

Introduction 介绍 In this lesson we’re going to see how we can build neural networks capable of learning the complex kinds of relationships deep neural nets are famous for. 在本课中，我们将了解如何构建著名的能够学习复杂关系的深度神经网络。 The key idea here is modularity, building up a complex network from simpler functional units. We’ve seen how a linear unit computes a linear function […]