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While reading the the book on neural network http://neuralnetworksanddeeplearning.com/chap2.html by Michael Nielson I had a problem of understanding eqn BP3. Which reads as "Change in cost wrt bias in a neuron is equals to error in that neuron". (Sorry unable to put the eqn here.)

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This is just an application of the chain rule. The same chapter has "Proof of the four fundamental equations" section, which proves BP1-2, while PB3-4 are left as exercise to the reader. I agree that it's a good exercise indeed, that's why I encourage you to stop here and try to prove it yourself using a chain rule.

Now, if you decided to read further, here's the sketch of a proof.

Recall equations (25) and (29), both definitions:

  • $z^l$ is a linear transformation of $a^{l-1}$: $z^l = w^l a^{l-1} + b^l$

  • $\delta^l$ is a partial derivative of $C$ with respect to $z^l$: $\delta^l = \dfrac{\partial C}{\partial z^l}$

The chain rule itself:

  • the partial derivative of $C$ with respect to $b^l$:

$$\frac{\partial C}{\partial b^l_j} = \sum_k \frac{\partial C}{\partial z^l_k} \frac{\partial z^l_k}{\partial b^l_j}$$

Almost all elements in this sum will be zero, except for one when $k=j$. The first term in it is by definition $\delta^l_j$, the second term $\dfrac{\partial z^l_j}{\partial b^l_j}$ is $1$, because $z$ is linear with respect to $b$.

The derivative with respect to weights is taken the same way, and differs only in the last term: $z$ is linear with respect to $w$ as well, but with a coefficient $a^{l-1}$.

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