All you need to know about the state of the art Transformer Neural Network Architecture, adapted to Time Series Tasks. Keras code included.

Image by romnyyepez from Pixabay

Table of Contents


An organised codebase enables you to implement changes faster and make less mistakes, ultimately leading to higher code and model quality. Read more to learn how to structure your ML projects with Tensorflow Extended (TFX), the easy and straightforward way.

Image by Francis Ray from Pixabay

Project Structure: Requirements

Project Structure: Design Decisions

Quit depending on positional indices and input value ordering. Start relying on named inputs and outputs. Avoiding data wiring errors

Image by Daniel Dino-Slofer from Pixabay


Namely: Hyperparameter Search, Convolution Variants, Network-in-Network, Weight Sharing, Pruning, Quantization

Picture by stevepb on Pixabay


Hyperparameter Search

Extracting labels, windowing multivariate series, multiple TF Record file shards and other useful tips for dealing with sequential data

Image by stux from Pixabay

Windowing Labelled Data

a, b
1, 0
2, 0
3, 1
4, 0
5, 0
6, 1

A creative PoseNet application that runs on your browser and tries to predict if you’re jumping, crouching, or staying still

Screenshot from

Overcoming Tech Barriers

A quick api overview and a self-contained example of fluent-tfx

Image by Michal Jarmoluk from Pixabay

But Tensorflow Extended is already fully capable to construct e2e pipelines by itself, why bother to use another API ?

Why it exists and how it’s used in Beam Pipeline Components

Image from

A practical and self-contained example using GCP Dataflow

Picture from

What’s going to be covered

Motivation, intuition and the process behind this series of articles

Theodoros Ntakouris

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store