@article{928727,
      recid = {928727},
      author = {Paluszek, Michael. and Thomas, Stephanie.},
      title = {Practical MATLAB deep learning : a project-based approach  /},
      publisher = {Apress,},
      address = {Berkeley, CA :},
      pages = {1 online resource (260 pages)},
      year = {2020},
      abstract = {Harness the power of MATLAB for deep-learning challenges.  This book provides an introduction to deep learning and  using MATLAB's deep-learning toolboxes. Youll see how these  toolboxes provide the complete set of functions needed to  implement all aspects of deep learning. Along the way,  you'll learn to model complex systems, including the stock  market, natural language, and angles-only orbit  determination. Youll cover dynamics and control, and  integrate deep-learning algorithms and approaches using  MATLAB. You'll also apply deep learning to aircraft  navigation using images. Finally, you'll carry out  classification of ballet pirouettes using an inertial  measurement unit to experiment with MATLAB's hardware  capabilities. You will: Explore deep learning using MATLAB  and compare it to algorithms Write a deep learning function  in MATLAB and train it with examples Use MATLAB toolboxes  related to deep learning Implement tokamak disruption  prediction.},
      url = {http://library.usi.edu/record/928727},
      doi = {https://doi.org/10.1007/978-1-4842-5},
}