\[ \begin{align}\begin{aligned}\newcommand{\ba}{\boldsymbol{a}} \newcommand{\bb}{\boldsymbol{b}} \newcommand{\be}{\boldsymbol{e}} \newcommand{\bw}{\boldsymbol{w}} \newcommand{\bx}{\boldsymbol{x}} \newcommand{\by}{\boldsymbol{y}} \newcommand{\bz}{\boldsymbol{z}} \newcommand{\bd}{\boldsymbol{d}} \newcommand{\bv}{\boldsymbol{v}} \newcommand{\bs}{\boldsymbol{s}}\\\newcommand{\btheta}{\boldsymbol{\theta}} \newcommand{\bbeta}{\boldsymbol{\beta}} \newcommand{\bgamma}{\boldsymbol{\gamma}} \newcommand{\bsigma}{\boldsymbol{\sigma}} \newcommand{\md}{\mbox{d}} \newcommand{\bmu}{\boldsymbol{\mu}} \newcommand{\bone}{\boldsymbol{1}} \newcommand{\trans}{^{\rm\scriptsize T}} \newcommand{\var}{\mathrm{var}}\\\newcommand{\bA}{\boldsymbol{A}} \newcommand{\bB}{\boldsymbol{B}} \newcommand{\bC}{\boldsymbol{C}} \newcommand{\bD}{\boldsymbol{D}} \newcommand{\bI}{\boldsymbol{I}} \newcommand{\bM}{\boldsymbol{M}} \newcommand{\bW}{\boldsymbol{W}} \newcommand{\bX}{\boldsymbol{X}} \newcommand{\bY}{\boldsymbol{Y}} \newcommand{\bZ}{\boldsymbol{Z}} \newcommand{\cotp}{\textcolor{ #30D158FF }{TP}} \newcommand{\cotn}{\textcolor{#64D2FFFF}{TN}} \newcommand{\cofp}{\textcolor{#5E5CE6FF}{FP}} \newcommand{\cofn}{\textcolor{#BF5AF2FF}{FN}}\\\newcommand{\numcotp}{\textcolor{ #30D158FF }{50}} \newcommand{\numcotn}{\textcolor{#64D2FFFF}{30}} \newcommand{\numcofp}{\textcolor{#5E5CE6FF}{10}} \newcommand{\numcofn}{\textcolor{#BF5AF2FF}{10}}\end{aligned}\end{align} \]

课程课件

英文课件列表

1. Chapter 1: Introduction

   • Course Introduction (Slides , PDF )。

2. Chapter 2: Fully-Connected Neural Networks

   • 2-1 Neural Network with One Hidden Layer I (Slides , PDF );

   • 2-2 Neural Network with One Hidden Layer II (Slides , PDF )

   • 2-3 Activation Function (Slides , PDF )

   • 2-4 Gradient Descent Algorithms (Slides , PDF )

   • 2-5 Neural Network with Multiple Hidden Layers (Slides , PDF )

   • 2-6 Evaluation of Binary Classification Models (Slides , PDF )

   • 2-7 Softmax Regression (Slides , PDF )

3. Chapter 3: Model Analysis

   • 3-1 Bias-Variance Analysis (Slides , PDF )

   • 3-2 Regularization (Slides , PDF )

   • 3-3 Batch Normalization （Slides , PDF )

4. Chapter 4: Convolution Neural Networks

   • 4-1 Convolution (Slides , PDF )

   • 4-2 Convolution Neural Network I (Slides , PDF )

   • 4-3 Convolution Neural Network II (Slides , PDF )

   • 4-4 More Examples (Slides , PDF )

   • 4-5 ResNet (Slides , PDF )

5. Chapter 5: Sequential Models

   • 5-1 Introduction (Slides , PDF )

   • 5-2 More Recurrent Neural Networks (Slides , PDF )

   • 5-3 Transformers (Slides , PDF )

6. Chapter 6 (To be continued)