‘Artificial’ neural network (ANN), is a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.
ANNs are processing devices (algorithms or actual hardware) that are loosely modeled after the neuronal structure of the mamalian cerebral cortex but on much smaller scales. A large ANN might have hundreds or thousands of processor units, whereas a mamalian brain has billions of neurons with a corresponding increase in magnitude of their overall interaction and emergent behavior. Although ANN researchers are generally not concerned with whether their networks accurately resemble biological systems, some have.
Real-valued Circuits
Base Case: Single Gate in the Circuit
- Random Local Search
- Numerical Gradient
- Analytic Gradient
Recursive Case: Circuits with Multiple Gates
Backpropagation
Patterns in the “backward” flow
Numerical Gradient Check
#Deep Dive into Math Behind Deep Networks
Nowadays, having at our disposal many high-level, specialized libraries and frameworks such as Keras, TensorFlow or PyTorch,
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