https://github.com/yuehhua/deep-learning.jl

Introduction to deep learning using Flux.jl
https://github.com/yuehhua/deep-learning.jl

Last synced: about 1 month ago
JSON representation

Introduction to deep learning using Flux.jl

• Host: GitHub
• URL: https://github.com/yuehhua/deep-learning.jl
• Owner: yuehhua
• Created: 2018-06-24T04:19:05.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2020-06-16T14:59:57.000Z (over 4 years ago)
• Last Synced: 2024-11-02T07:33:23.068Z (about 2 months ago)
• Language: Jupyter Notebook
• Size: 747 KB
• Stars: 20
• Watchers: 3
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Deep Learning Demo

Introduction to deep learning using Flux.jl in JuliaLang.

The following examples are derived and modified from the [model zoo](https://github.com/FluxML/model-zoo) of [Flux.jl](https://github.com/FluxML/Flux.jl).

1. [Design layer](notebook/00_Flux_syntax.ipynb)

2. [Linear regression](notebook/01_Linear_regression.ipynb)

3. [Autoencoder](notebook/02_Autoencoder.ipynb)

4. [Multi-layer perceptron](notebook/03_Multi-layer_perceptron.ipynb)

5. [Convolutional neural network](notebook/04.1_CNN-MNIST.ipynb)

6. [Convolutional neural network using CIFAR-10](notebook/4.2_CNN-CIFAR-10.ipynb)

7. [Recurrent neural network](notebook/05_RNN.ipynb)

8. [Attention mechanism]()

9. [Variational autoencoder](notebook/07_Variational_autoencoder.ipynb)

10. [β-VAE](notebook/08_Beta-VAE.ipynb)

11. [Using pre-trained model](notebook/09_Pre-trained_model.ipynb)

12. [Transfer learning](notebook/10_Transfer_learning.ipynb)

## Deep learning models

* [ObjectDetector.jl](https://github.com/r3tex/ObjectDetector.jl)

* [Transformers.jl](https://github.com/chengchingwen/Transformers.jl)

* [GeometricFlux.jl](https://github.com/yuehhua/GeometricFlux.jl)

* [DiffEqFlux.jl](https://github.com/SciML/DiffEqFlux.jl)

* [Flux3D.jl](https://github.com/nirmal-suthar/Flux3D.jl)