Flashield's Blog

Just For My Daily Diary

Flashield's Blog

Just For My Daily Diary

Python

04.course-overfitting-and-underfitting【过拟合与欠拟合】

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 […]

03.exercise-stochastic-gradient-descent【练习:随机梯度下降】

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。 […]

03.course-stochastic-gradient-descent【随机梯度下降】

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 […]

02.exercise-deep-neural-networks【练习:深度神经网络】

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 […]

02.course-deep-neural-networks【深度神经网络】

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 […]

01.exercise-a-single-neuron【练习:单神经元】

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 […]

01.course-a-single-neuron【单神经元】

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 […]

05.exercise-inconsistent-data-entry【练习:数据不一致】

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. […]

05.course-inconsistent-data-entry【数据不一致】

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 […]

04.exercise-character-encodings【练习:字符编码】

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 […]

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