Developing a The right time Series Prediction Product by having parsnip & XGBoost | two (2) of two

The 2nd part of a tutorial from the #Shiny Web Apps Course – This video is from the Demand #Forecast section where our students build a predictive model to forecast sales demand with #parsnip & #XGBoost. Learn more:

Get the ML Cheat Sheet with XGBoost Hyperparameters here:

Seq2seq Product on Time-series Data: Exercising and training and Having by using TensorFlow that in fact Masood Krohy

Masood Krohy at April 9, 2019 event of

Title: Seq2seq Model on Time-series Data: Training and Serving with TensorFlow

Summary: Seq2seq models are a class of Deep Learning models that have provided state-of-the-art solutions to language problems recently. They also perform very well on numerical, time-series data which is of particular interest in finance and IoT, among others. In this hands-on demo/code walkthrough, we explain the model development and optimization with TensorFlow (its low-level API). We then serve the model with TensorFlow Serving and show how to write a client to communicate with TF Serving over the network and use/plot the received predictions.

Code on GitHub:

Bio: Masood Krohy is a Data Science Platform Architect/Advisor and most recently acted as the Chief Architect of UniAnalytica, an advanced data science platform with wide, out-of-the-box support for time-series and geospatial use cases. He has worked with several corporations in different industries in the past few years to design, implement and productionize Deep Learning and Big Data products. He holds a Ph.D. in computer engineering.

This video is a production of PatternedScience Inc.
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