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channels:
- conda-forge
dependencies:
##### Core scientific packages
- python >=3.0
- jupyter==1.0.0
- pip
- matplotlib==3.0.3
- numpy==1.16.2
- pandas==0.24.1
- scipy==1.2.1
##### Machine Learning packages
- scikit-learn==0.20.3
- xgboost==0.82
##### Deep Learning packages
# Replace tensorflow with tensorflow-gpu if you want GPU support. If so,
# you need a GPU card with CUDA Compute Capability 3.0 or higher support, and
# you must install CUDA, cuDNN and more: see tensorflow.org for the detailed
# installation instructions.
- tensorflow==1.13.1
#- tensorflow-gpu==1.13.1
# Optional: OpenAI gym is only needed for the Reinforcement Learning chapter.
# There are a few dependencies you need to install first, check out:
# https://github.com/openai/gym#installing-everything
#- pip:
#- gym[all]==0.10.9
# If you only want to install the Atari dependency, uncomment this line instead:
#- gym[atari]==0.10.9
##### Image manipulation
- imageio==2.5.0
- pillow==6.2.0
- scikit-image==0.14.2
##### Extra packages (optional)
# Nice utility to diff Jupyter Notebooks.
#- nbdime==1.0.5
# May be useful with Pandas for complex "where" clauses (e.g., Pandas
# tutorial).
- numexpr==2.6.9
# Optional: these libraries can be useful in the classification chapter,
# exercise 4.
- nltk==3.4.5
- pip:
- urlextract==0.9
# Optional: tqdm displays nice progress bars, ipywidgets for tqdm's notebook support
- tqdm==4.31.1
- ipywidgets==7.4.2
# Optional: Some useful extensions to customize and configure jupyter notebooks
- jupyter_contrib_nbextensions
- jupyter_nbextensions_configurator
name: mlbook
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