Normalization, division and reshaping processes on an HDF5 file
It seems that all your code could be vectorized with the help of numpy broadcasting.

At first, instead of using all these norm_vec[:, ...] = ... you could create two vectors of length 17 containing values you use to normalize the data. 

I assume normalizing values to be mean and standard deviation (please tell me if I'm wrong), so I'll be calling them mean and std correspondingly. 

mean is a np.ndarray with values [-3.059, -1.5707, ..., -0.05] and std is a np.ndarray with values [6.117, 3.6647, ..., 2.67] (indicies ranging from zero to 16).

Using this notation, for loop could be rewritten:

for j in range(len(hdf5_file["vector"])):

        norm_vec = (hdf5_file["vector"][j] - mean) / std

        reshaped_vec = norm_vec.reshape(500 * 17)
        jointMatrix = np.vstack((jointMatrix, reshaped_vec))


This should give a certain speed-up. However, the code could be further optimized by vectorizing the loop itself. 

The whole code now looks like this:

def pre_proc_data():

    hdf5_file = h5py.File("/home/Data.hdf5")
    norm_vec = (hdf5_file["vector"] - mean) / std
    # from 3d to 2d
    return norm_vec.reshape(-1, 500 * 17)

jointMatrix = pre_proc_data()