All Projects → QuantScientist → Deep Learning Boot Camp

QuantScientist / Deep Learning Boot Camp

Licence: mit
A community run, 5-day PyTorch Deep Learning Bootcamp

Programming Languages

python
139335 projects - #7 most used programming language

Projects that are alternatives of or similar to Deep Learning Boot Camp

Pytorch Kaggle Starter
Pytorch starter kit for Kaggle competitions
Stars: ✭ 268 (-78.9%)
Mutual labels:  kaggle-competition, kaggle, jupyter-notebook, pytorch-tutorial
My Journey In The Data Science World
📢 Ready to learn or review your knowledge!
Stars: ✭ 1,175 (-7.48%)
Mutual labels:  kaggle-competition, kaggle, jupyter-notebook, data-science
Kaggle Competitions
There are plenty of courses and tutorials that can help you learn machine learning from scratch but here in GitHub, I want to solve some Kaggle competitions as a comprehensive workflow with python packages. After reading, you can use this workflow to solve other real problems and use it as a template.
Stars: ✭ 86 (-93.23%)
Mutual labels:  kaggle-competition, kaggle, jupyter-notebook, data-science
4th Place Home Credit Default Risk
Codes and dashboards for 4th place solution for Kaggle's Home Credit Default Risk competition
Stars: ✭ 23 (-98.19%)
Mutual labels:  kaggle, jupyter-notebook, data-science
Deep Recommender System
深度学习在推荐系统中的应用及论文小结。
Stars: ✭ 657 (-48.27%)
Mutual labels:  kaggle, jupyter-notebook, data-science
Pyopencl
OpenCL integration for Python, plus shiny features
Stars: ✭ 790 (-37.8%)
Mutual labels:  nvidia, gpu, cuda
Cudasift
A CUDA implementation of SIFT for NVidia GPUs (1.2 ms on a GTX 1060)
Stars: ✭ 555 (-56.3%)
Mutual labels:  nvidia, gpu, cuda
Cuda
Experiments with CUDA and Rust
Stars: ✭ 31 (-97.56%)
Mutual labels:  nvidia, gpu, cuda
Cub
Cooperative primitives for CUDA C++.
Stars: ✭ 883 (-30.47%)
Mutual labels:  nvidia, gpu, cuda
Machinelearningcourse
A collection of notebooks of my Machine Learning class written in python 3
Stars: ✭ 35 (-97.24%)
Mutual labels:  kaggle, jupyter-notebook, data-science
Optix Path Tracer
OptiX Path Tracer
Stars: ✭ 60 (-95.28%)
Mutual labels:  nvidia, gpu, cuda
Parenchyma
An extensible HPC framework for CUDA, OpenCL and native CPU.
Stars: ✭ 71 (-94.41%)
Mutual labels:  nvidia, gpu, cuda
H2o 3
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
Stars: ✭ 5,656 (+345.35%)
Mutual labels:  jupyter-notebook, data-science, gpu
Trtorch
PyTorch/TorchScript compiler for NVIDIA GPUs using TensorRT
Stars: ✭ 583 (-54.09%)
Mutual labels:  nvidia, jupyter-notebook, cuda
Data Science Competitions
Goal of this repo is to provide the solutions of all Data Science Competitions(Kaggle, Data Hack, Machine Hack, Driven Data etc...).
Stars: ✭ 572 (-54.96%)
Mutual labels:  kaggle-competition, kaggle, data-science
Kaggle Web Traffic Time Series Forecasting
Solution to Kaggle - Web Traffic Time Series Forecasting
Stars: ✭ 29 (-97.72%)
Mutual labels:  kaggle-competition, kaggle, jupyter-notebook
Hyperlearn
50% faster, 50% less RAM Machine Learning. Numba rewritten Sklearn. SVD, NNMF, PCA, LinearReg, RidgeReg, Randomized, Truncated SVD/PCA, CSR Matrices all 50+% faster
Stars: ✭ 1,204 (-5.2%)
Mutual labels:  jupyter-notebook, data-science, gpu
Ai Lab
All-in-one AI container for rapid prototyping
Stars: ✭ 406 (-68.03%)
Mutual labels:  nvidia, data-science, cuda
Caer
High-performance Vision library in Python. Scale your research, not boilerplate.
Stars: ✭ 452 (-64.41%)
Mutual labels:  data-science, gpu, cuda
Keras Pytorch Avp Transfer Learning
We pit Keras and PyTorch against each other, showing their strengths and weaknesses in action. We present a real problem, a matter of life-and-death: distinguishing Aliens from Predators!
Stars: ✭ 42 (-96.69%)
Mutual labels:  jupyter-notebook, pytorch-tutorial, pytorch-tutorials

Deep Learning Winter School, November 2107.

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

cuda

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

cuda

Meetup:

https://www.meetup.com/TensorFlow-Tel-Aviv/events/241762893/

Registration:

https://www.eventbrite.com/e/5-day-deep-learning-bootcamp-november-2017-als-fund-raising-tickets-37001430274

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

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.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].