February 2024
Intermediate to advanced
256 pages
4h 1m
Chinese
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第6章 面向预测分析的神经网络简介
本章主要内容
● 神经网络简介。
● TensorFlow和Keras简介。
● 基于神经网络的回归模型。
● 基于神经网络的分类模型。
● 一些应用神经网络的重要实用技巧。
我们在第4章和第5章介绍了一些面向回归任务和分类任务的常用模型。在本章中，我们会引入神经网络模型。这一类模型是深度学习的基础，而深度学习是人工智能领域中近年来发展较快的机器学习方法。
我们将详细介绍基于神经网络的预测分析。这里的要点是模型的基本概念，以及学习如何训练基本的神经网络类型——多层感知器（Multilayer Perceptron，MLP）。
在讨论MLP结构时，我们首先会介绍神经网络的主要概念，然后讨论如何学习这些模型进行预测。介绍完概念，我们将带领大家学习TensorFlow，特别是Keras——它是本章构建模型和训练模型所使用的主要工具。我们将继续使用本书中处理过的两个数据集，并看一看神经网络做出的预测是否会更好，最后总结神经网络模型的相关训练问题，讨论一些重要的相关技术，比如早期停止和dropout正则化。
学完本章后，我们就可以把MLP引入自己的预测分析工具箱。
6.1 技术要求
● Python 3.6或更高版本。
● Jupyter Notebook。
● 最新版本的Python库：NumPy、pandas、matplotlib、Seaborn和scikit-learn。
● TensorFlow和Keras的最新版本。
6.2 引入神经网络模型
近年来，神经网络和深度学习等术语无疑吸引了人们的大量关注。尽管这些技术难免存在炒作和误解之嫌，但它们依然是人工智能领域中飞速发展和取得重大突破的部分，应用领域涉及自动驾驶汽车、语言翻译器、语音识别、计算机视觉，以及各种与深度模型有直接关系的许多其他成果。 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.