Transfer learning with Tensorflow

Jan 16, 2018 · Berlin, Germany

• What we'll do
Transfer learning with Tensorflow (or how to avoid training from scratch like the plague)

We'll get started with Tensorflow with a simple computer vision project that can be adapted to many use cases. We will train a model to recognize different varieties of objects. It is meant for people who have Python experience, perhaps already Machine Learning experience (or at least interest in it :) ) but none at all with Deep Learning frameworks. In this session we won't re-write each training block in Tensorflow code yet, but we will go over essential concepts in an image classification task and what is going on inside each block.

• What we assume
- You are used to coding in Python.
- You have a Linux system (OSX is fine; Windows with Anaconda should be ok, but I cannot guarantee at all).
- You already have a clean Python virtual environment ready (both 2 and 3 will work to avoid a flamewar - but I encourage you to use Python 3 ;-) ).

• Preparation
Wifi may not be the best when we're all there, so come prepared and download these couple of things beforehand...

* Data: We'll use the very common and lovely:

http://download.tensorflow.org/example_images/flower_photos.tgz

You'll recognise this one from many tutorials :)

* Libraries: Tensorflow.

Tensorflow 1.3+ is required. Python 3 is advised.

Install the requirements:
requirements.txt (Save the lines between === in a file named requirements.txt)
==============================================
bleach==1.5.0
enum34==1.1.6
html5lib==[masked]
Markdown==2.6.10
numpy==1.13.3
protobuf==3.5.1
six==1.11.0
tensorflow==1.4.1
tensorflow-tensorboard==0.4.0rc3
Werkzeug==0.13
===============================================
Install with:
pip install -r requirements.txt

Don't worry if you don't have a GPU or a powerful CPU, the trainings will be quite light and can be run even on a Mac.

Speaker: Irina Vidal Migallón
Bio:
Irina is an Electrical Engineer turned Computer Vision engineer, seasoned in different industries: from optical biopsy systems in France to surgical planning tools or VR/AR apps in the Berlin start-up scene. She's recently joined Siemens Mobility's growing CV & AI team. Even more than waking up Skynet, she's interested in the limits of Natural Intelligence and its decisions over our data.

• Gender policy
We want to make sure that we have at least 50% female attendees, so we ask our non female attendees to come with a female plus one.

• What to bring
laptop

Event organizers
  • PyLadies Berlin

    PyLadies is an international mentorship group with a focus on helping more women become active participants and leaders in the Python open-source community. Our mission is to promote, educate and advance a diverse Python community through outreach, education, conferences, events and social gatherings. PyLadies also aims to provide a friendly support network for women and a bridge to the larger Python world. Anyone with an interest in Python is encouraged to participate! Pyladies Berlin are regularly meetin

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