Deep Learning with Julia

DL with Julia is a book about how to do various deep learning tasks using the Julia programming language and specifically the Flux.jl package. The intent of the book is to prove that serious deep learning can be done in Julia and that the ecosystem as a whole is ready for the spotlight.

Getting Started

All of the code and resources for this book are stored here on GitHub and deployed to https://deeplearningwithjulia.com.

Content

At the present moment, my focus is on writing materials in the following areas:

[] Basic Recurrent Neural Networks with Flux.jl (WIP)
[] CNN Basics with Flux.jl
[] Transfer Learning for Computer Vision with Flux.jl (WIP)
[] Solving basic NLP problems with Flux.jl
[] Preparing and using data with Flux.jl (inspired by out image augmentation assignment somewhat)
[] Saving and loading machine learning models in Flux.jl
[] Automatic Differentiation in Flux (https://www.microsoft.com/en-us/research/video/the-simple-essence-of-automatic-differentiation/)

The Deep Learning with Julia book, using Flux.jl.

Related tags

Overview

Deep Learning with Julia

Getting Started

Content

Owner

Logan Kilpatrick

Unofficial Tensorflow Implementation of ConvNeXt from A ConvNet for the 2020s

Self-Supervised Contrastive Learning of Music Spectrograms

A pytorch reprelication of the model-based reinforcement learning algorithm MBPO

TensorFlow implementation of ENet

Data cleaning, missing value handle, EDA use in this project

code for TCL: Vision-Language Pre-Training with Triple Contrastive Learning, CVPR 2022

Official Pytorch implementation of "Learning Debiased Representation via Disentangled Feature Augmentation (Neurips 2021, Oral)"

Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome

Blender Add-on that sets a Material's Base Color to one of Pantone's Colors of the Year

NeuPy is a Tensorflow based python library for prototyping and building neural networks

Code for our paper "Multi-scale Guided Attention for Medical Image Segmentation"

This implements the learning and inference/proposal algorithm described in "Learning to Propose Objects, Krähenbühl and Koltun"

The Official TensorFlow Implementation for SPatchGAN (ICCV2021)

Chess reinforcement learning by AlphaGo Zero methods.

Human annotated noisy labels for CIFAR-10 and CIFAR-100.

Repository for the electrical and ICT benchmark model developed in the ERIGrid 2.0 project.

Online Pseudo Label Generation by Hierarchical Cluster Dynamics for Adaptive Person Re-identification

This is the source code for our ICLR2021 paper: Adaptive Universal Generalized PageRank Graph Neural Network.

Pytorch implementation of MaskGIT: Masked Generative Image Transformer

Whisper is a file-based time-series database format for Graphite.