4-1 Introduction to Neural Network - Neural Network and Deep learning | Coursera

4-1 Introduction to Neural Network

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National Taiwan University

人工智慧：機器學習與理論基礎 (Artificial Intelligence - Learning & Theory)

National Taiwan University

4.6 (48 ratings)

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本課程第二部分著重在和人工智慧密不可分的機器學習。課程內容包含了機器學習基礎理論（包含 1990 年代發展的VC理論）、分類器（包含決策樹及支援向量機）、神經網路（包含深度學習）及增強式學習（包含深度增強式學習。此部份技術包含最早追溯至 1950 年代直到最近 2016 年附近的最新發展。此課程從基礎理論開始，簡介了各機器學習主流技法以及從淺層學習架構演變到最近深度架構的轉換。本課程之核心目標為：（一）使同學對人工智慧相關的機器學習技術有基礎概念（二）同學能夠理解機器學習基礎理論、分類器、神經網路、增強式學習（三）同學能將相關技術應用到自己的問題上修課前，基礎背景知識：需要的先備知識：計算機概論建議的先備知識：資料結構與演算法

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4.6 (48 ratings)

5 stars
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4 stars
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3 stars
6.25%
1 star
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KJ

Jan 3, 2022

Professor Ding's teaching is conscientious and the lectures are clearly explained

EC

Aug 6, 2019

整體上, 是值得推薦的入門課程, 把machine learning的基本課程與熱門的topics提出來講. 習題的內容算簡單, 大部份在檢驗觀念.

From the lesson

Neural Network and Deep learning

4-1 Introduction to Neural Network22:15

4-2 Single-Layer Network and Perceptron Learning Rule15:50

4-3 Multi-Layer Perceptron, Back Propagation Learning, Decline of ANN10:20

4-4 Cascade Correlation Neural Networks, Deep or Shallow Structure23:22

4-5 Deep Learning: Convolutional Neural Networks17:17

4-6 LeNet 5, Dropout, ReLU and the Variants, Maxout, Residual Net18:21

4-7 Recurrent Networks, Long Short-Term Memory (LSTM), Neural Turing Machine, Memory-Augmented Neural Networks (MANN)15:18

4-8 Autoencoder: Denoising Autoencoder, Stacked Autoencoder and Variational Autoencoder12:08

4-9 Generative Adversarial Net (GAN), AE+GAN and Its Applications16:00

Taught By

于天立
副教授 (Associate Professor)

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