Upgrade libraries to latest versions
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#
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# Next, optionally uncomment the OpenAI gym lines (see below).
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# If you do, make sure to install the dependencies first.
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# If you are interested in xgboost for high performance Gradient Boosting, you
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# should uncomment the xgboost line (used in the ensemble learning notebook).
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#
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# Then install these requirements:
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# $ pip install --upgrade -r requirements.txt
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##### Core scientific packages
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jupyter==1.0.0
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matplotlib==2.2.2
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numpy==1.14.2
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numpy==1.14.3
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pandas==0.22.0
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scipy==1.0.1
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# Optional: these are useful Jupyter extensions, in particular to display
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# the table of contents.
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jupyter-contrib-nbextensions==0.5.0
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scipy==1.1.0
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##### Machine Learning packages
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scikit-learn==0.19.1
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# Optional: the XGBoost library is only used in the ensemble learning chapter.
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#xgboost==0.71
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##### Deep Learning packages
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# Replace tensorflow with tensorflow-gpu if you want GPU support.
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# You also need to install CUDA, cuDNN and more, see tensorflow.org for the
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# detailed installation instruction.
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tensorflow==1.7.0
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# Replace tensorflow with tensorflow-gpu if you want GPU support. If so,
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# you need a GPU card with CUDA Compute Capability 3.0 or higher support, and
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# you must install CUDA, cuDNN and more: see tensorflow.org for the detailed
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# installation instructions.
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tensorflow==1.8.0
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#tensorflow-gpu==1.8.0
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# Forcing bleach to 1.5 to avoid version incompatibility when installing
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# TensorBoard.
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bleach==1.5.0
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Keras==2.1.6
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Keras==2.1.5
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# Optional: OpenAI gym is only needed for the Reinforcement Learning chapter.
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# There are a few dependencies you need to install first, check out:
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@ -55,10 +63,25 @@ Keras==2.1.5
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#gym[atari]==0.10.5
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##### Extra utilities
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bleach==1.5.0
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nbdime==0.4.1
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numexpr==2.6.4
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##### Image manipulation
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imageio==2.3.0
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Pillow==5.1.0
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psutil==5.4.3
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sympy==1.1.1
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scikit-image==0.13.1
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##### Extra packages (optional)
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# Nice utility to diff Jupyter Notebooks.
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nbdime==0.4.1
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# May be useful with Pandas for complex "where" clauses (e.g., Pandas
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# tutorial).
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numexpr==2.6.5
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# These libraries can be useful in the classification chapter, exercise 4.
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nltk==3.3
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urlextract==0.8.3
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# Optional: these are useful Jupyter extensions, in particular to display
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# the table of contents.
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jupyter-contrib-nbextensions==0.5.0
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