  In cosmological analyses, precise redshift determination remains pivotal for
understanding cosmic evolution. However, with only a fraction of galaxies
having spectroscopic redshifts (spec-$z$s), the challenge lies in estimating
redshifts for a larger number. To address this, photometry-based redshift
(photo-$z$) estimation, employing machine learning algorithms, is a viable
solution. Identifying the limitations of previous methods, this study focuses
on implementing deep learning (DL) techniques within the Kilo-Degree Survey
(KiDS) Bright Galaxy Sample for more accurate photo-$z$ estimations. Comparing
our new DL-based model against prior `shallow' neural networks, we showcase
improvements in redshift accuracy. Our model gives mean photo-$z$ bias $\langle
\Delta z\rangle= 10^{-3}$ and scatter $\mathrm{SMAD}(\Delta z)=0.016$, where
$\Delta z = (z_\mathrm{phot}-z_\mathrm{spec})/(1+z_\mathrm{spec})$. This
research highlights the promising role of DL in revolutionizing photo-$z$
estimation.
