TALK-TensorFlow-Roadshow-Zurich

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TALK-TensorFlow-Roadshow-Zurich

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This repository contains the material for the TensorFlow Roadshow for Zurich

TALK-TensorFlow-Roadshow-Zurich 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Repository for the TensorFlow Roadshow in Zürich - 16th Jan. 2020

![](https://img.shields.io/badge/dependencies-TensorFlow20-blue)
![](https://img.shields.io/badge/dependencies-Jupyter-red)
[![license MIT](https://img.shields.io/badge/license-MIT-green.svg)](https://opensource.org/licenses/MIT)

This repository contains material, notebooks, scripts about Edge Computing and TensorFlow Lite.

Note that this repository will use relative linking. That means that
all the documentations (markdown files) can be accessed directly
from your laptop without the need of having access to internet
to get files from [GitHub](https://github.com/michelucci/Edge-computing-with-TensorFlow), once you have cloned the repository
on your machine. Of course it can be completely accessed directly
online from [GitHub](https://github.com/michelucci/Edge-computing-with-TensorFlow).

The material is divided in sections, each having its own folder.
In each folder you will find a `README.md` with a table of content and instructions of what you can find in the folder. Here you will find a high level table of content that should help you in navigating the material and finding what you need or search.

Mostly the material is presented with three different media types:

1. **Jupyter notebooks or Python scripts**:  those are scripts that can
be used to try and learn. The notebooks and scripts are mostly used for
the hands-on sessions.

2. **Google Slides**: the slides linked in this repository are not meant
to contain all the material in details, but are used as a guideline.
They show the reader/learner the main points that are important.

3. **Markdown files in the repository**: for some more technical material,
that is important and that requires more explanation I have provided
some markdown files that can be accessed directly online with more information
on specific technical issues.

# Slides

Slides for this workshop can be found here

Part 1: [TensorFlow 2.0 Introduction](https://docs.google.com/presentation/d/1x5hCQOkgXsTvWOVwU6Kf3tn1RxPY14kObxz9pR7q1GQ/edit#slide=id.g6d95d55f5c_0_1150)

Part 2: [A TPU primer](https://docs.google.com/presentation/d/1itxw8EGG-K0LI-jrczWfZnWcsWM2a4zBkqGukVQ5QNw/edit?usp=sharing)

Part 3: [TensorFlow Lite introduction](https://docs.google.com/presentation/d/1tyHy5kmu-f2vq4gcT-ny9eQVxk4BqL6scFRtfeFbtDw/edit?usp=sharing)

TensorFlow Ecosystem: **Author**: Fabien Tarrade @fabtar (contact@fabien-tarrade.eu)

TensorFlow Ecosystem Slides: **TensorFlow Ecosystem, Machine Learning: From Development To Production** [link](https://github.com/michelucci/TensorFlow-Roadshow-Zurich/blob/master/Meetup_Zurich-TF_ecosystem-F_Tarrade-16_01_2020.pdf)

## Table of Content of material covered in Meetup

### Chapter 1 - Introduction and setup

[1.1 Introduction](1-Introduction/1-1-Introduction.md)

[1.2 Setup of Coral Device on MAC OS Catalina](1-Introduction/1-2-Setup-of-Coral-Catalina.md)

### Chapter 2 - Hardware Accelerators

[2.1 How a TPU works: a very high level overview](2-Hardware_Accelerators/2-1-TPU.md)

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