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

When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset of 53,000+ Legal Holdings

Active and Sample-Efficient Model Evaluation

Optimizing Deeper Transformers on Small Datasets

[ICLR 2021 Spotlight Oral] "Undistillable: Making A Nasty Teacher That CANNOT teach students", Haoyu Ma, Tianlong Chen, Ting-Kuei Hu, Chenyu You, Xiaohui Xie, Zhangyang Wang

The pytorch implementation of SOKD (BMVC2021).

TimeSHAP explains Recurrent Neural Network predictions.

Using deep learning to predict gene structures of the coding genes in DNA sequences of Arabidopsis thaliana

Manifold Alignment for Semantically Aligned Style Transfer

Official implementation of VQ-Diffusion

ALBERT-pytorch-implementation - ALBERT pytorch implementation

Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors, CVPR 2021

SoGCN: Second-Order Graph Convolutional Networks

Swapping face using Face Mesh with TensorFlow Lite

A GOOD REPRESENTATION DETECTS NOISY LABELS

Code for "Multi-Compound Transformer for Accurate Biomedical Image Segmentation"

A tutorial showing how to train, convert, and run TensorFlow Lite object detection models on Android devices, the Raspberry Pi, and more!

Tianshou - An elegant PyTorch deep reinforcement learning library.

Source Code and data for my paper titled Linguistic Knowledge in Data Augmentation for Natural Language Processing: An Example on Chinese Question Matching

Direct design of biquad filter cascades with deep learning by sampling random polynomials.

Easy-to-use micro-wrappers for Gym and PettingZoo based RL Environments