D2l Zh Pytorch Colab Chapter Preliminaries Pandas Ipynb At Main D2l

There was an error while loading. Please reload this page. To prepare for your dive into deep learning, you will need a few survival skills: (i) techniques for storing and manipulating data; (ii) libraries for ingesting and preprocessing data from a variety of sources;... In short, this chapter provides a rapid introduction to the basics that you will need to follow most of the technical content in this book. 我的笔记本miniconda运行不了更深的神经网络了，于是决定重新配置一下colab 先新建一个文件夹，叫Colab Notebooks。然后进入文件夹后，空白处鼠标右键“更多”，选择”Google Colaboratory“，这时候就有一个新的了。

再在”/content/drive/MyDrive“路径下，新建一个文件夹”d2l-zh"，运行代码。 There was an error while loading. Please reload this page. So far, we have been working with synthetic data that arrived in ready-made tensors. However, to apply deep learning in the wild we must extract messy data stored in arbitrary formats, and preprocess it to suit our needs. Fortunately, the pandas library can do much of the heavy lifting.

This section, while no substitute for a proper pandas tutorial, will give you a crash course on some of the most common routines. Comma-separated values (CSV) files are ubiquitous for the storing of tabular (spreadsheet-like) data. In them, each line corresponds to one record and consists of several (comma-separated) fields, e.g., “Albert Einstein,March 14 1879,Ulm,Federal polytechnic school,field of gravitational physics”. To demonstrate how to load CSV files with pandas, we create a CSV file below ../data/house_tiny.csv. This file represents a dataset of homes, where each row corresponds to a distinct home and the columns correspond to the number of rooms (NumRooms), the roof type (RoofType), and the price (Price). Now let’s import pandas and load the dataset with read_csv.

In supervised learning, we train models to predict a designated target value, given some set of input values. Our first step in processing the dataset is to separate out columns corresponding to input versus target values. We can select columns either by name or via integer-location based indexing (iloc). You might have noticed that pandas replaced all CSV entries with value NA with a special NaN (not a number) value. This can also happen whenever an entry is empty, e.g., “3,,,270000”. These are called missing values and they are the “bed bugs” of data science, a persistent menace that you will confront throughout your career.

Depending upon the context, missing values might be handled either via imputation or deletion. Imputation replaces missing values with estimates of their values while deletion simply discards either those rows or those columns that contain missing values. Automatically Generated d2l-zh PyTorch Notebooks for Colab

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