Deep Learning Neural Network & Neuron Basics Under 3 min Loss Activation Weights Bias Optimizer Pass

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Ep- 2 - Neural Network Basic Neuron Under 3 min - Loss Activation Weights Bias Optimizer

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A) Loss Function = Loss functions measure how far an estimated value is from its true value.
B) Activation Function = activation function of a node defines the output of that node given an input or set of inputs.
f [ Sum{(weight * input)} + bias ]
C) BIAS: Bias is just like an intercept added in a linear equation. It is an additional parameter in the Neural Network which is used to adjust the output along with the weighted sum of the inputs to the neuron. Moreover, the bias value allows you to shift the activation function to either right or left.
D) Forward Pass: The "forward pass" refers to the calculation process, values of the output layers from the inputs data. It's traversing through all neurons from the first to the last layer.
E) Backward Pass: then "backward pass" refers to the process of counting changes in weights (de facto learning), using gradient descent algorithm (or similar). Computation is made from the last layer, backward to the first layer.
F) Iteration: Backward and forward passes make together one "iteration".
G) Batch / mini-batch: During one iteration, you usually pass a subset of the data set, which is called "mini-batch" or "batch" (however, "batch" can also mean an entire set, hence the prefix "mini")
H) Epoch: Epoch means passing the entire data set in batches.
One epoch contains (number_of_items / batch_size) iterations. Forward pass and backward pass together form an epoch.
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