What are the differences between `numpy.random.rand` and `numpy.random.randn`?

From the documentation, I know the only difference between them is the probabilistic distribution each number is drawn from, but the overall structure (dimension) and data type used (float) is the same. I have a hard time debugging a neural network because of this.

Specifically, I am trying to re-implement the Neural Network provided in the [Neural Network and Deep Learning book by Michael Nielson](http://neuralnetworksanddeeplearning.com/chap1.html). The original code can be found [here](https://github.com/mnielsen/neural-networks-and-deep-learning/blob/master/src/network.py). My implementation was the same as the original; however,  I instead defined and initialized weights and biases with `numpy.random.rand` in the `init` function, rather than the `numpy.random.randn`  function as shown in the original. 

However, my code that uses `random.rand` to initialize `weights and biases` does not work. The network won&#39;t learn and the weights and biases will not change.

What is the difference(s) between the two random functions that cause this weirdness?


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Differences between numpy.random.rand vs numpy.random.randn in Python