PhotoLabellerServer

Deeplearning4j Examples (DL4J, DL4J Spark, DataVec)

This project contains examples which demonstrate how to deploy analytic models to mission-critical, scalable production environments leveraging Apache Kafka and its Streams API. Models are built with Python, H2O, TensorFlow, Keras, DeepLearning4 and other technologies.

目标是提供一个完整的Java机器学习(Machine Learning/ML)框架,作为人工智能在学术界与工业界的桥梁. 让相关领域的研发人员能够在各种软硬件环境/数据结构/算法/模型之间无缝切换. 涵盖了从数据处理到模型的训练与评估各个环节,支持硬件加速和并行计算,是最快最全的Java机器学习库.

An independent implementation of DeepMind's AlphaGoZero in Scala, using Deeplearning4J (DL4J)

Federated Learning: Client application doing classification of images and local training. Works better with the Parameter Server at https://github.com/mccorby/PhotoLabellerServer

Using Capsule Networks in GANS to generate very realistic fake images that could perhaps be used for deepfakes

See new version https://github.com/mccorby/PhotoLabeller

Making custom object detector using Yolo (Java and Python)

Pujangga - Indonesian Natural Language Processing Tool with REST API, an Interface for InaNLP and Deeplearning4j's Word2Vec

VAE with RealNVP prior and Super-Resolution VAE in PyTorch. Code release for https://arxiv.org/abs/2006.05218.