Tel Aviv Deep Learning Bootcamp : http://deep-ml.com
Contact: Shlomo Kashani at shlomo@bayesian.io
Tel-Aviv Deep Learning Bootcamp is an intensive (and free!) 5-day program intended to teach you all about deep learning. It is nonprofit focused on advancing data science education and fostering entrepreneurship. The Bootcamp is a prominent venue for graduate students, researchers, and data science professionals. It offers a chance to study the essential and innovative aspects of deep learning.
Participation is via a donation to the A.L.S ASSOCIATION for promoting research of the Amyotrophic Lateral Sclerosis (ALS) disease.
You can register, however we reserve no places, folowing a first come first serve policy.
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Ubuntu Linux 16.04, Mac OSX or Windows 10
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Python 3.5 or above
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CUDA 9.0 drivers.
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cuDNN 7.0.
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pytorch >= 0.2.0
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torchvision >= 0.1.8
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Keras
Keep in mind that this repository expects data to be in same format as Imagenet. I encourage you to use your own datasets. In that case you need to organize your data such that your dataset folder has EXACTLY two folders. Name these 'train' and 'val'
The 'train' folder contains training set and 'val' fodler contains validation set on which accuracy / log loss is measured.
The structure within 'train' and 'val' folders will be the same. They both contain one folder per class. All the images of that class are inside the folder named by class name; this is crucial in PyTorch.
If your dataset has 2 classes like in the Kaggle Statoil set, and you're trying to classify between pictures of 1) ships 2) Icebergs, say you name your dataset folder 'data_directory'. Then inside 'data_directory' will be 'train' and 'test'. Further, Inside 'train' will be 2 folders - 'ships', 'icebergs'.
|- data_dir
|- train
|- ships
|- ship_image_1
|- ship_image_2
.....
|- ice
|- ice_image_1
|- ice_image_1
.....
|- val
|- ships
|- ice
For a full example refer to: https://github.com/QuantScientist/Deep-Learning-Boot-Camp/blob/master/Kaggle-PyTorch/PyTorch-Ensembler/kdataset/seedings.py
This project has been realised with PyCharm by JetBrains
Shlomo Kashani/ @QuantScientist and many more.