AI & Machine Learning

Concepts

Roadmap

Linear Algebra

Data Normalization

Hardware

MLOps

TODO Hypernetworks

TODO Embeddings

multimodal ML

from scratch

Model Parallelism

Distributed Computing for AI

Avatars

Experiments

Web3 + AI

Agents

Frameworks

TensorFlow

Keras

Kubeflow

DeepSpeed

Architecture Patterns

Linear Models

Neural Networks Models

Unsupervised Learning Models

Generative Models

Diffusion Models

Transformers

XGBoost

Final Applications

Computer Vision

Natural Language Processing (NLP)

TODO book Probabilistic Machine Learning Advanced Topics

TODO book Mathematical Foundations of Infinite-Dimensional Statistical Models

TODO book Designing Machine Learning Systems An Iterative Process for Production-Ready Applications

TODO book Information Theory, Inference, and Learning Algorithms

COURSES:

Deep Learning Course - You can find here slides, recordings, and a virtual machine for François Fleuret’s deep-learning courses 14x050 of the University of Geneva, Switzerland.

https://fleuret.org/dlc/

PAPERS:

https://paperswithcode.com/

https://github.com/dair-ai/ML-Papers-Explained

REPOSITORIES:

https://stanford.edu/~shervine/teaching/cs-229/

https://ml-cheatsheet.readthedocs.io/en/latest/index.html

https://www.deeplearningbook.org/

https://tivadardanka.com/book/

https://fleuret.org/dlc/

https://deeplearningmath.org/

https://udlbook.github.io/udlbook/

https://mml-book.github.io/

https://www.sscardapane.it/alice-book

ALGORITHMS:

https://www.algorithm-archive.org/

EXPLAINED VISUALLY:

https://qtnx.ai/posts/how_neuron_learns/

https://setosa.io/ev/

https://colah.github.io/

https://distill.pub/

https://poloclub.github.io/cnn-explainer/

https://stanford.edu/~shervine/teaching/

https://mlu-explain.github.io/

https://github.com/afshinea/stanford-cs-229-machine-learning/tree/master/en

NOTES:

https://github.com/sw-yx/prompt-eng

https://github.com/dair-ai/ML-Course-Notes

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