params = {}
    index = 1
    # Skip first element by using 1:
    for layerdim in conf['layer_dimensions'][1:]:
        variance = np.sqrt(2 / conf['layer_dimensions'][index-1])
        # Mean is automatically set to 0, so I don't have to specify np.random.normal(mean, var, size)
        params['W_' + str(index)] = np.random.normal(scale=variance, size=(conf['layer_dimensions'][index-1], layerdim))
        params['b_' + str(index)] = np.zeros(layerdim)
        index += 1
    return params