Deep Learning Winter School, November 2107.
Tel Aviv Deep Learning Bootcamp : http://deep-ml.com.
About
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.
Curriculum
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
Meetup:
https://www.meetup.com/TensorFlow-Tel-Aviv/events/241762893/
Registration:
Requirements
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'
Dependencies
- For a GPU based docker 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.
IDE
This project has been realised with PyCharm by JetBrains
Relevant info:
http://deep-ml.com/assets/5daydeep/#/3/1
Author
Shlomo Kashani/ @QuantScientist and many more.