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References

Artificial Neural Networks (ANNs) and Deep Learning

NYU Center for Data Science:

Course Material
Deep Learning - DS-GA 1008 Spring 2020 | Summary

Natural Language Processing

Stanford University

Course Material
CS224N: Natural Language Processing with Deep Learning
Speech and Language Processing
GloVe: Global Vectors for Word Representation
Learning Visual N-Grams from Web Data

Stanford University School of Engineering

Lecture Subject
2 Word Vector Representations: word2vec
3 GloVe: Global Vectors for Word Representation
4 Word Window Classification and Neural Networks
5 Backpropagation and Project Advice
6 Dependency Parsing
7 Introduction to TensorFlow
8 Recurrent Neural Networks and Language Models
9 Machine Translation and Advanced Recurrent LSTMs and GRUs
Review Session Midterm Review
10 Neural Machine Translation and Models with Attention
11 Gated Recurrent Units and Further Topics in NMT
12 End-to-End Models for Speech Processing
13 Convolutional Neural Networks
14 Tree Recursive Neural Networks and Constituency Parsing
15 Coreference Resolution
16 Dynamic Neural Networks for Question Answering
17 Issues in NLP and Possible Architectures for NLP
18 Tackling the Limits of Deep Learning for NLP

https://github.com/jtrecenti/2025-verao-torch

Latent Space Visualisation: PCA, t-SNE, UMAP UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction Visualizing Data using t-SNE How to Use t-SNE Effectively An Introduction to Variational Autoencoders Autoencoders, Unsupervised Learning, and Deep Architectures Autoencoders Generative Adversarial Networks (GANs) Generative Adversarial Networks (GANs) - Deep Learning Specialization Generative Adversarial Networks (GANs) - Paper GANs in Action: Deep learning with Generative Adversarial Networks

https://mathigon.org/timeline/shannon

DSA - Data Structures and Algorithms

TutorialsPoint - Data Structures and Algorithms

GeeksforGeeks - Data Structures and Algorithms

David Galles - Data Structure Visualizations

Antti Laaksonen - Competitive Programmer’s Handbook

Gayle Laakmann McDowell, Mike Mroczka, Aline Lerner - Beyond Cracking the Coding Interview

To explore for the next version:

MIT - Deep Learning 6 - 7960 suggested by Miranda

Harvard - CS249R Book suggested by Miranda

KAN: Kolmogorov-Arnold Networks

Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset

https://pyimagesearch.com/2025/03/24/fastapi-meets-openai-clip-build-and-deploy-with-docker/

https://pyimagesearch.com/2023/10/02/a-deep-dive-into-variational-autoencoders-with-pytorch/

https://lilianweng.github.io/posts/2018-10-13-flow-models/

https://en.wikipedia.org/wiki/Evidence_lower_bound

https://github.com/black-forest-labs/flux

https://www.youtube.com/watch?v=eMlx5fFNoYc

https://github.com/cloudedrepo/Cloud-Computing-Curricula/blob/master/Core/KAs/KAs.md

https://cloudedrepo.github.io/Cloud-Computing-Curricula/Core/KAs/KAs.html

https://ai.meta.com/blog/code-llama-large-language-model-coding/

https://avandekleut.github.io/vae/#motivation

https://github.com/ageron/handson-ml3

Lora