Conclusion

  • The Python scientific computing environment keeps evolving
    • IPython development (Data Science team at Berkeley)
    • Scikit-learn (INRIA team)
    • Lots of contributions every year, spoted at SciPy Conference
  • New arrivals: deep learning
    • Theano
      • Expressions involving NumPy arrays efficiently computed in GPUs
    • Keras and PyTorch
      • Deep Neural Networks

In his book, Prince argues that computer vision should be understood in terms of measurements (images), the world state, a model (defining the statistical relationships between the observations and the world), parameters, and learning and inference algorithms. The presented environment can address all these elements in modern computer vision R&D. Also, such a environment keeps evolving. For example, in the raising field of deep learning vision, packages as Keras, PyTorch, and Theano are promising tools that could become an important part of the Python ecosystem for scientific computing in a near future.

Where to go from here?

Thank you

Questions?