TF_JAX_tutorials
uvadlc_notebooks
| TF_JAX_tutorials | uvadlc_notebooks | |
|---|---|---|
| 1 | 2 | |
| 258 | 2,151 | |
| - | - | |
| 0.0 | 6.6 | |
| over 2 years ago | 13 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
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TF_JAX_tutorials
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JAX Tutorials [D]
Someone already gave the links https://github.com/AakashKumarNain/TF_JAX_tutorials
uvadlc_notebooks
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I miss the old days where people asked me to recreate “Facebook” or “Twitter”
So, I don’t have anything simple that’s readily available, and I don’t know how much you’d get from the code itself without some background. But I would recommend the UVA Deep Learning tutorials. Particularly, I’d recommend trying the autoencoder as a good start (tutorial 9). Autoencoders are very easy and fast models to train.
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[D] Favorite Colab Notebooks / runnable tutorials on adversarial CV
DL course from the University of Amsterdam:Github and Colab including another FGSM example
What are some alternatives?
TensorFlow-Examples - TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
tensor-sensor - The goal of this library is to generate more helpful exception messages for matrix algebra expressions for numpy, pytorch, jax, tensorflow, keras, fastai.
equinox - Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
hyper-nn - Easy Hypernetworks in Pytorch and Jax
get-started-with-JAX - The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning about the JAX ecosystem.
pyprobml - Python code for "Probabilistic Machine learning" book by Kevin Murphy
dynamax - State Space Models library in JAX
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.
jaxrl - JAX (Flax) implementation of algorithms for Deep Reinforcement Learning with continuous action spaces.
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱