来自台湾国立清华大学吴尚鸿副教授主讲的《大规模机器学习》教程，内容包括深度学习概述与学习理论。
本课程介绍深度学习的概念和实践。课程由三个部分组成。在第一部分中，我们快速介绍了经典机器学习，并回顾了一些需要理解深度学习的关键概念。在第二部分中，我们将讨论深度学习与经典机器学习的不同之处，并解释为什么它在处理复杂问题如图像和自然语言处理时是有效的。我们将介绍各种CNN和RNN模型。在第三部分，我们介绍了深度强化学习及其应用。
本课程也提供了编程的实验。在整个课程中，我们将使用Python 3作为主要的编程语言。一些流行的机器学习库，如Scikit-learn和Tensorflow 2.0将被使用并详细解释。
本课程也提供了编程的实验。在整个课程中，我们将使用Python 3作为主要的编程语言。一些流行的机器学习库，如Scikit-learn和Tensorflow 2.0将被使用并详细解释。
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Introduction 引言
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Linear Algebra 线性代数
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Data Exploration & PCA (Bonus) 数据探索
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Probability & Information Theory 概率与信息理论
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Decision Trees & Random Forest (Bonus) 决策树与随机森林
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数值优化 Numerical Optimization
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感知器 Perceptron & Adaline (Bonus)
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回归 Regression (Bonus)
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学习理论与正则 Learning Theory & Regularization
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正则化 Regularization
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概率模型 Probabilistic Models
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线性回归与度量 Logistic Regression & Metrics
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非参数方法 Non-Parametric Methods & SVMs (Suggested Reading)
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支持向量机 SVMs & Scikit-Learn Pipelines (Bonus)
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交叉验证 Cross Validation & Ensembling (Suggested Reading)
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集成 CV & Ensembling (Bonus)
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预测 Predicting News Popularity
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大规模机器学习 Large-Scale Machine Learning
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深度神经网络设计 Neural Networks: Design
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神经网络 Neural Networks from Scratch (No Assignment)
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TensorFlow 101 (No Assignment)
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神经网络 Neural Networks: Optimization & Regularization
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Word2Vec
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卷积神经网络 Convolutional Neural Networks
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Convolutional Neural Networks & Data Pipelines
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循环神经网络 Recurrent Neural Networks
视频教程地址
https://nthu-datalab.github.io/ml/
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