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Deep Learning with R by François Chollet with J. J. Allaire

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Chapter 2. Before we begin: the mathematical building blocks of neural networks

This chapter covers

  • A first example of a neural network
  • Tensors and tensor operations
  • How neural networks learn via backpropagation and gradient descent

Understanding deep learning requires familiarity with many simple mathematical concepts: tensors, tensor operations, differentiation, gradient descent, and so on. Our goal in this chapter will be to build your intuition about these notions without getting overly technical. In particular, we’ll steer away from mathematical notation, which can be off-putting for those without any mathematics background and isn’t strictly necessary to explain things well.

To add some context for tensors and gradient descent, we’ll ...

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