Pytorch array to tensor
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The material presented resulted from the work performed by the responsible partners: this implies a literature review and field research, based on their knowledge and experience,
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Національний технічний університет України «Київський політехнічний інститут імені Ігоря Сікор
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This approach postulates harnessing the co-designing methodology. As architectures and sizes of a neural processor fits only classes of problems, first, building and optimizing a neural network model with the tools available today: Tensor Flow, PyTorch or Caffe2 frameworks needs to be done.
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Optional: Recurrent Neural Nets and LSTMs with PyTorch . Requirements: NumPy, pandas, Machine Learning fundamentals. Time required: 4 hours. Introduction to PyTorch . Introduction to tensor algebra and calculus. Tensor algebra in PyTorch. Training and validating models. Regression in PyTorch . Optimizers in PyTorch
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