Just For My Daily Diary
Just For My Daily Diary
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 […]
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 learned about the building blocks of neural networks: linear units. We saw that a model of just one linear unit will fit a linear function to a dataset (equivalent […]
Welcome to Deep Learning! 欢迎来到深度学习！ Welcome to Kaggle’s Introduction to Deep Learning course! You’re about to learn all you need to get started building your own deep neural networks. Using Keras and Tensorflow you’ll learn how to: 欢迎来到 Kaggle 的深度学习简介课程！ 您将学习开始构建自己的深度神经网络所需的所有知识。 使用 Keras 和 Tensorflow，您将学习如何： create a fully-connected neural network architecture 创建全连接神经网络架构 apply neural nets […]
This notebook is an exercise in the Data Cleaning course. You can reference the tutorial at this link. In this exercise, you’ll apply what you learned in the Inconsistent data entry tutorial. 在本练习中，您将应用在数据不一致教程中学到的知识。 Setup 设置 The questions below will give you feedback on your work. Run the following cell to set up the feedback system. […]
In this notebook, we’re going to learn how to clean up inconsistent text entries. 在本笔记本中，我们将学习如何清理不一致的文本条目。 Let’s get started! 让我们开始吧！ Get our environment set up 设置我们的环境 The first thing we’ll need to do is load in the libraries and dataset we’ll be using. 我们需要做的第一件事是加载我们将使用的库和数据集。 # pip install fuzzywuzzy, python-Levenshtein # modules we'll use import pandas as […]
This notebook is an exercise in the Data Cleaning course. You can reference the tutorial at this link. In this exercise, you’ll apply what you learned in the Character encodings tutorial. 在本练习中，您将应用在 字符编码 教程中所学到的知识。 Setup 设置 The questions below will give you feedback on your work. Run the following cell to set up the feedback […]
In this notebook, we’re going to be working with different character encodings. 在本笔记本中，我们将使用不同的字符编码。 Let’s get started! 让我们开始吧！ Get our environment set up 设置我们的环境 The first thing we’ll need to do is load in the libraries we’ll be using. Not our dataset, though: we’ll get to it later! 我们需要做的第一件事是加载我们将使用的库。 不过，这不是我们的数据集：我们稍后会处理它！ # modules we'll use import pandas […]
Time-series plotting (Optional) 时间序列绘图（可选） In all of the sections thus far our visualizations have focused on and used numeric variables: either categorical variables, which fall into a set of buckets, or interval variables, which fall into an interval of values. In this notebook we will explore another type of variable: a time-series variable. 到目前为止，在所有部分中，我们的可视化都集中在并使用了数字变量：要么是属于一组用桶(buckets)存储的 calcategories […]