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 - [Calculus for ML](https://ml-cheatsheet.readthedocs.io/en/latest/calculus.html) cheat sheet
 - [Linear Algebra Review](/fa24-nmep/assets/resources/linalg-review.pdf)
 
-### Neural Networks
-- [CS197 Harvard](https://docs.google.com/document/d/1dA8KmOTZePMRl3MixxM6Fb0H8IJhIyn_g-LUXbRHeqU): Moonwalking with PyTorch
+### 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!)
 
-### Computer Vision
-- JPEG through the lens of signal processing ([YouTube](https://www.youtube.com/watch?v=0me3guauqOU))
+### 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
+- [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
 - [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
+- [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
+- [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)
 
 ### Papers
 - [How to read a paper](https://web.stanford.edu/class/ee384m/Handouts/HowtoReadPaper.pdf)