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

Vector Quantization, in Pytorch

Tensorflow implementation of DeepLabv2

Graph neural network message passing reframed as a Transformer with local attention

Code for Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty

Adversarial Graph Augmentation to Improve Graph Contrastive Learning

Use Python, OpenCV, and MediaPipe to control a keyboard with facial gestures

Hyperopt for solving CIFAR-100 with a convolutional neural network (CNN) built with Keras and TensorFlow, GPU backend

An efficient 3D semantic segmentation framework for Urban-scale point clouds like SensatUrban, Campus3D, etc.

Automated Hyperparameter Optimization Competition

This repo is customed for VisDrone.

Layered Neural Atlases for Consistent Video Editing

Consistency Regularization for Adversarial Robustness

CUda Matrix Multiply library.

Multi-Glimpse Network With Python

GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models

gtfs2vec - Learning GTFS Embeddings for comparing PublicTransport Offer in Microregions

[ArXiv 2021] One-Shot Generative Domain Adaptation

A Fast Monotone Rotating Shallow Water model

Learning from Guided Play: A Scheduled Hierarchical Approach for Improving Exploration in Adversarial Imitation Learning Source Code

Toward Realistic Single-View 3D Object Reconstruction with Unsupervised Learning from Multiple Images (ICCV 2021)