Development environment in Docker, which contains the most important tools for:
- Scientific Computing
- Statistics
- Neural Networks
- Evolutionary Computing
- Machine Learning
- Deep Learning
- Data analysis
- Time Series Analysis
- Data Visualization
- Natural Language Processing
- Computer Vision
Below, a part of the tools contained in the environment
- Numpy
- Pandas
- Anaconda
- Jupyter
- Scikit-learn
- Tensorflow
- Python
- R
- Julia
- NLTK
- Opencv
- Keras
- Matplotlib
- Deap
- SciPy
- Fbprophet
- Scikit-Image
- Scikit-fuzzy
First of all, build the container using docker-compose and then you can access the Jupyter that is ready to be used.
cd datascience-quick-start
docker-compose up -d --build
http://<your-ip>:8888/tree
- 8888 => Jupyter
- 6007 => Tensorboard
- 5007 => Your App
https://hub.docker.com/r/maciomatheus/jupyter_notebook_data_science/
Add support for
- Theano
- PyTorch
- Tensorflow GPU (nvidia-docker)