From 712b364140d7a96e610187995d830e66d068e347 Mon Sep 17 00:00:00 2001 From: Tanmayi Dasari <69954364+tanmayi2@users.noreply.github.com> Date: Thu, 20 Mar 2025 12:46:00 -0700 Subject: [PATCH] Update resources.md added week-specific headers --- docs/resources.md | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/docs/resources.md b/docs/resources.md index b71d393..31add45 100644 --- a/docs/resources.md +++ b/docs/resources.md @@ -6,49 +6,49 @@ layout: home ### [Server Form](https://forms.gle/zybsuJ7CwQCXeHni6) -### HW 0 (Math) +### Week 1: Background Review (Math) - [Princeton NEU 314](http://pillowlab.princeton.edu/teaching/mathtools16/slides/lec10_LeastSquaresRegression.pdf) Sections 1 through 2.1 (Least squares with vector calculus) - [Stanford cheat sheets](https://stanford.edu/~shervine/teaching/) on ML and math - [Calculus for ML](https://ml-cheatsheet.readthedocs.io/en/latest/calculus.html) cheat sheet - [Linear Algebra Review](/fa24-nmep/assets/resources/linalg-review.pdf) -### Intro to ML +### Week 1: Intro to ML - [Pandas API reference](https://pandas.pydata.org/pandas-docs/stable/reference/index.html) - [Numpy API reference](https://numpy.org/doc/stable/reference/) - [Data 100](https://ds100.org) (specifically the [course notes](https://ds100.org/course-notes/pandas_1/pandas_1.html)) - [CS197 Harvard](https://docs.google.com/document/d/1dA8KmOTZePMRl3MixxM6Fb0H8IJhIyn_g-LUXbRHeqU): Moonwalking with PyTorch (neural networks!) -### Classical ML +### Week 2: Classical ML - [Bias and Variance](https://www.youtube.com/watch?v=EuBBz3bI-aA) - [Gradient Descent](https://www.youtube.com/watch?v=qg4PchTECck) - [A Guided Tour Through Classical Machine Learning Algorithms](https://learn.mathnai.com/module/ml/guided-tour-classical-ml-algorithms/) -### Deep Learning +### Week 3: Deep Learning - [3Blue1Brown Course](https://youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi&si=q1tAqWSEEH612y2C) - [Deep Dive on Back Prop Video](https://youtu.be/SmZmBKc7Lrs?si=f4kFtNGFCjf54Cxc) - [Optimizers](https://www.youtube.com/watch?v=MD2fYip6QsQ) - [Micrograd - Try to Build Backprop Yourself](https://www.youtube.com/watch?v=VMj-3S1tku0&t=1s) (by Andrej Karpathy!) -### Convultions & CNNs +### Week 4: Convoltions & CNNs - [Understanding Convolutions](https://drive.google.com/drive/folders/1e4C7s3pEPt2lLirIu02DiUdKlKlvsMo5?usp=sharing) (old NMEP HW!) - [CNNs Visualized](https://www.youtube.com/watch?v=pj9-rr1wDhM) - [Simple CNN Explanation](https://www.youtube.com/watch?v=zfiSAzpy9NM) - [MIT Convolutional Neural Networks](https://youtu.be/NmLK_WQBxB4?t=335&feature=shared) - [Convolutional Neural Networks](https://d2l.ai/chapter_convolutional-neural-networks/lenet.html) (LeNet) -### Object Detection -- [Understanding of Object Detection](https://iopscience.iop.org/article/10.1088/1742-6596/1004/1/012029/pdf) -- [A comprehensive review of object detection](https://www.sciencedirect.com/science/article/pii/S1051200422004298) -- [Deep learning models for classification](https://metana.io/blog/deep-learning-models-for-classification-a-comprehensive-guide/) - -### Transformers +### Week 5 & 6: Transformers - [Data C182](https://datac182fa24.github.io/) (part [2](https://datac182fa24.github.io/assets/lecture_slides/data182_Lecture12_Transformers_Part2.pdf) and part [4](https://datac182fa24.github.io/assets/lecture_slides/data182_Lecture14_Transformers_Part4.pdf) of the lecture on transformers) - [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/) - [Constituency Parsing](https://nlpprogress.com/english/constituency_parsing.html) - [Machine Translation Evaluation Metrics](https://aclanthology.org/2023.wmt-1.96.pdf) - [Show, Attend, and Tell: Neural Image Caption Generation](https://arxiv.org/abs/1502.03044) (all about visual transformers) -### GANs +### Week 8: Object Detection +- [Understanding of Object Detection](https://iopscience.iop.org/article/10.1088/1742-6596/1004/1/012029/pdf) +- [A comprehensive review of object detection](https://www.sciencedirect.com/science/article/pii/S1051200422004298) +- [Deep learning models for classification](https://metana.io/blog/deep-learning-models-for-classification-a-comprehensive-guide/) + +### Week 10: GANs - [Foundational Paper](https://arxiv.org/pdf/1406.2661) - [Unsupervised Representational Learning with GANs](https://arxiv.org/pdf/1511.06434) - [Generating Basketball Shoes](https://connorshorten300.medium.com/generating-basketball-shoes-with-dcgans-6cd72d521c01)