gd121286 Goto Github PK

actor_critic

alphatensor

annotated_deep_learning_paper_implementations

🧑‍🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

awesome-notebooks

Ready to use data science templates, organized by tools to jumpstart your projects and data products in minutes. 😎 published by the Naas community.

chess_endings_reinforcement_learning

clase-1---introducci-n-a-python

clase-2--probabilidad

clase-3---clasificadores-bayesianos

cnn

Convolutional Neural Networks

cogvideo

Text-to-video generation.

dalle-mini

DALL·E Mini - Generate images from a text prompt

dalle2-inference

Explore the main modules involved in the inference process of the DALL-E 2 openAI architecture.

dataviz

A book covering the fundamentals of data visualization

eda-home-credit

efficient_python_tricks_and_tools_for_data_scientists

Efficient Python Tricks and Tools for Data Scientists

fairseq

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

flores

Facebook Low Resource (FLoRes) MT Benchmark

following-instructions-human-feedback

frame-interpolation

FILM: Frame Interpolation for Large Motion, In arXiv 2022.

gan_steerability

On the "steerability" of generative adversarial networks

geniverse_hub

glide-text2im

GLIDE: a diffusion-based text-conditional image synthesis model

go

The Open Source Data Science Masters

google-research

Google Research

handson_ml_2nd

Note for Aurélien Géron's 2019 book "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition"

holovision

Config files for my GitHub profile.

image_classification_using_transfer_learning

Building Image classifier using pre-trained neural network, VGG model

infovis

Diplomatura Big Data

instant-ngp

Instant neural graphics primitives: lightning fast NeRF and more

intro-probabilidad

Introducción a las nociones de Probabilidad Condicional y el Teorema de Bayes a través de dos aplicaciones: Localización y Naive Bayes