January 19, 2019

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A community run, 5-day PyTorch Deep Learning Bootcamp

repo name QuantScientist/Deep-Learning-Boot-Camp
repo link https://github.com/QuantScientist/Deep-Learning-Boot-Camp
homepage http://deep-ml.com
language Jupyter Notebook
size (curr.) 329455 kB
stars (curr.) 1209
created 2017-07-19
license MIT License

Deep Learning Winter School, November 2107.

Tel Aviv Deep Learning Bootcamp : http://deep-ml.com.



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.


The Bootcamp amalgamates “Theory” and “Practice” – identifying that a deep learning scientist desires a survey of concepts combined with a strong application of practical techniques through labs. Primarily, the foundational material and tools of the Data Science practitioner are presented via Sk-Learn. Topics continue rapidly into exploratory data analysis and classical machine learning, where the data is organized, characterized, and manipulated. From day two, the students move from engineered models into 4 days of Deep Learning.

Bootcamp 5 day structure

The Bootcamp consists of the following folders and files:

  • day 01: Practical machine learning with Python and sk-learn pipelines

  • day 02 PyTORCH and PyCUDA: Neural networks using the GPU, PyCUDA, PyTorch and Matlab

  • day 03: Applied Deep Learning in Python

  • day 04: Convolutional Neural Networks using Keras

  • day 05: Applied Deep Reinforcement Learning in Python

  • docker: a GPU based docker system for the bootcamp

Click to view the full CURRICULUM : http://deep-ml.com/assets/5daydeep/#/3/1







For a docker based system See https://github.com/QuantScientist/Data-Science-ArrayFire-GPU/tree/master/docker

  • Ubuntu Linux 16.04
  • Python 2.7
  • CUDA drivers.Running a CUDA container requires a machine with at least one CUDA-capable GPU and a driver compatible with the CUDA toolkit version you are using.

The HTML slides were created using (You can run this directly from Jupyter):

%%bash jupyter nbconvert \ --to=slides \ --reveal-prefix=https://cdnjs.cloudflare.com/ajax/libs/reveal.js/3.2.0/ \ --output=py05.html \ './05 PyTorch Automatic differentiation.ipynb'



This project has been realised with PyCharm by JetBrains

Relevant info:



Shlomo Kashani/ @QuantScientist and many more.

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