#ML #NLP #Recommender
> ML: Tensorflow, Keras, Xgboost,
> NLP: Rasa, spaCy, Gensim, GluonNLP, GloVe
> Web: Flask, Django, Vue, SQL
> APP: Swift, Android Studio
> Others: Docker, docker-compose, Spark
In a corporation with a security company, they want to include some AI in their security system in an exhibition. There are two main of conditions. One is to apply in department store to recognize and record the flow of people with gender and age instantly. The other one is an access control by recognize face of people.
To their demand, we make a device embedding two machine learning model to detect face and recognize age and gender.
It is lucky to participate the design of recommender system of a top e-commerce platform, and that is the first time I took "big data". Because of the amount of data, the data pipeline need to be very careful in the parallel computing. We use Apache Spark framework and Kubernetes to deploy our models.
The recommender system is mainly combined by two models. One model is user-based model and the other is content-based model. We use fully-connected layer to combine.