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.

TensorDash is an application that lets you remotely monitor your deep learning model's metrics and notifies you when your model training is completed or crashed.

Sensor-based human activity recognition from smartphone data in Keras with on-device inference

How to run a Keras model on Android using Tensorflow API.

KNIME Deep Learning Integration

Fall detection Android app

I explain how to export weights from a Keras model and import those weights in Keras.js, a JavaScript framework for running pre-trained neural networks in the browser. I show you later how to include the final result into a Phonegap Cordova mobile application.

TensorFlow Keras训练手写数字识别集Mnist，生成tflite模型文件并在Android上使用示例

Audio classifier, encoder, and search engine in Java

A rubbish detection application, based on a neural network built with keras and optimized with TensorFlow Lite API

Keras-style machine learning framework for Java

Provide a web service that uses the trained model from the project NLP Text Classification.

AI Based Recruitments Android App

A Machine learning Android app that can able to classify whether a tomato leaf is healthy or infected leaf.

Deep Learning model using Convolutional Neural Network to classify a image into six categories

Running Keras CNN model on Android.

Pagen is a handy, lightweight, fast picture/HTML conversion tool, aiming to make writing HTML code easier. Pagen offers user two choices: directly upload picture or create a picture by themselves.

Using Java and deeplearning4j to serve a Keras model.