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

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Table of Contents

Introduction


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.

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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

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Benefits


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

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Motives

Hyperparameter Search


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

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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

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Overcoming Tech Barriers


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

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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 https://www.tensorflow.org/tfx/guide/mlmd


A practical and self-contained example using GCP Dataflow

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What’s going to be covered


Motivation, intuition and the process behind this series of articles

Theodoros Ntakouris

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