# 2020TechXAppliedDL ## Day 1 - Linear Algebra (57 mins) 1. (15 mins) Read pages 29-35, 40-43 of the excerpt [Linear Algebra - Deep Learning](http://www.deeplearningbook.org/contents/linear_algebra.html) 2. (10 mins) Read [Linear Algebra for Deep Learning](https://towardsdatascience.com/linear-algebra-for-deep-learning-506c19c0d6fa) 3. (11 mins with 1.25x) Watch video [Matrix-Matrix Multiplication - Andrew Ng](https://www.bilibili.com/video/BV1AD4y1Q7RH?p=14) 4. (9 mins with 1.25x) Watch video [Matrix multiplication properties - Andrew Ng](https://www.bilibili.com/video/BV1AD4y1Q7RH?p=15) 5. (5 mins) Try the below matrix multiplication example [here](http://matrixmultiplication.xyz/): ![image](http://www.sciweavers.org/tex2img.php?eq=%5Cbegin%7Bbmatrix%7D1%262%263%5C%5C4%265%266%5C%5C7%268%269%5Cend%7Bbmatrix%7D%5Cbegin%7Bbmatrix%7D5%263%5C%5C4%266%5C%5C2%269%5Cend%7Bbmatrix%7D&bc=White&fc=Black&im=png&fs=12&ff=modern&edit=0 "image") 6. (10 mins) Read 11.2.1 of [深度学习数学基础](https://zh.d2l.ai/chapter_appendix/math.html) 7. (Optional, 15 mins with 1.25x) [L1/4 Linear Algebra - Deep Learning UC Berkeley](https://www.bilibili.com/video/BV14t41147MX?p=4) ## Day 2 - Derivative, Gradient(41 mins) 1. (18 mins with 1.25x) Watch video [Derivative formulas through geometry - 3Blue1Brown](https://www.bilibili.com/video/BV1qW411N7FU?p=3) 2. (17 mins with 1.25x) Watch video [Visualizing the chain rule and product rule - 3Blue1Brown](https://www.bilibili.com/video/BV1qW411N7FU?p=4) 3. (11 mins with 1.25x) Watch video [Patial Derivative Introduction - Khan Academy](https://www.bilibili.com/video/BV1L7411a7Xk) 4. (5 mins with 1.25x) Watch video [Gradient and its calculation - Khan Academy](https://www.bilibili.com/video/BV1L7411a7Xk) 5. (10 mins) Read 11.2.2.1, 11.2.2.3, 11.2.2.4 of [深度学习数学基础 ](https://zh.d2l.ai/chapter_appendix/math.html) for better understanding ## Day 3 - Gradient Descent & Linear Regression (60 mins) 1. (8 mins) Watch [Cost Function - Andrew Ng](https://www.bilibili.com/video/BV1AD4y1Q7RH?p=5) 2. (11 mins) Watch [Gradient Descent - Andrew Ng](https://www.bilibili.com/video/BV1AD4y1Q7RH?p=5) 3. (12 mins) Watch [Gradient Descent Intuition - Andrew Ng](https://www.bilibili.com/video/BV1AD4y1Q7RH?p=5) 4. (10 mins) Watch [Gradient Descent for Linear Regression - Andrew Ng](https://www.bilibili.com/video/BV1AD4y1Q7RH?p=5) 5. (19 mins) Watch [But what *is* a Neural Network? | Deep learning, Part 1](https://www.bilibili.com/video/BV1bx411M7Zx) 6. (Optional, 10 mins) [Why the gradient is the direction of steepest ascent - Khan Academy](https://www.bilibili.com/video/BV1iE411K7qv)（optional，偏数学推导） ## Day 4 - Gradient Descent, Forward&BackPropogation (55 mins) 1. (21 mins with 1.25x) Watch video [Gradient Descent - 3Blue1Brown](https://www.bilibili.com/video/BV1bx411M7Zx) 2. (14 mins with 1.25x) Watch video [Feedforward propagation - 3Blue1Brown](https://www.bilibili.com/video/BV16x411V7Qg/?spm_id_from=333.788.videocard.0) 3. (10 mins with 1.25x) Watch video [Backpropagation - 3Blue1Brown](https://www.bilibili.com/video/BV16x411V7Qg?p=2) 4. (Optional) Watch video for better understanding [Backpropagation 1 - Andrew Ng](https://www.bilibili.com/video/BV1AD4y1Q7RH?p=50) 5. (Optional) Watch video for better understanding [Backpropagation 2 - Andrew Ng](https://www.bilibili.com/video/BV1AD4y1Q7RH?p=51) 6. (Optional) Read Feedforward propagation&Backpropagation [深度学习数学基础](https://zh.d2l.ai/chapter_deep-learning-basics/backprop.html) 7. (10 mins) Compile [Linear Regression](https://www.kaggle.com/init27/fastai-v3-lesson-2-sgd#) to understand how to update weight with Pytorch and have visulization. More details about Pytorch will be introduced in Module 3 ## Day 5 - Forward&BackPropogation Continue and more ML (50 mins) 1. (Continued from day 4, 10 mins) Compile [Linear Regression](https://www.kaggle.com/init27/fastai-v3-lesson-2-sgd#) 2. (5 mins) overfitting and underfitting 3. (12 mins with 1.25x) Watch video [PCA 1 - Andrew Ng](https://www.bilibili.com/video/BV1AD4y1Q7RH?p=81) 4. (15 mins with 1.25x) Watch video [PCA 2 - Andrew Ng](https://www.bilibili.com/video/BV1AD4y1Q7RH?p=82) 5. (15 mins) [PCA animation](https://setosa.io/ev/principal-component-analysis/) 6. (Optional, if you want to know more about PCA) [Eigenvalue&Eigenvector](https://www.bilibili.com/video/BV1ys411472E?p=14) 7. (Optional) [Eigenvalue&Eigenvector Animation](https://setosa.io/ev/principal-component-analysis/) 8. (Optional) [Eigenvalue&Eigenvector Math](https://www.mathsisfun.com/algebra/eigenvalue.html)