[부스트캠프 AI Tech 5기] Pre-Course : (18) 뉴럴 네트워크 - MLP

📖 Neural Networks

• “Neural networks are computing systems vaguely inspired by the biological neural networks that constitute animal brains.”

• Neural networks are function approximators that stack affine transformations followed by nonlinear transformations.

📖 Linear Neural Networks

• Let’s start with the most simple example.

• We compute the partial derivatives w.r.t. the optimization variables.
• Then, we iteratively update the optimization variables.

• Of course, we can handle multi dimensional input and output.
• One way of interpreting a matrix is to regard it as a mapping between two vector spaces.

📖 Beyond Linear Neural Networks

• What if we stack more?
• We need nonlinearity.

• Activation functions

📖 Multi-Layer Perceptron

• This class of architectures are often called multi-layer perceptrons.
• Of course, it can go deeper.

• What about the loss functions?

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