  We are on the verge of a revolutionary era in space exploration, thanks to
advancements in telescopes such as the James Webb Space Telescope
(\textit{JWST}). High-resolution, high signal-to-noise spectra from exoplanet
and brown dwarf atmospheres have been collected over the past few decades,
requiring the development of accurate and reliable pipelines and tools for
their analysis. Accurately and swiftly determining the spectroscopic parameters
from the observational spectra of these objects is crucial for understanding
their atmospheric composition and guiding future follow-up observations.
\texttt{TelescopeML} is a Python package developed to perform three main tasks:
1. Process the synthetic astronomical datasets for training a CNN model and
prepare the observational dataset for later use for prediction; 2. Train a CNN
model by implementing the optimal hyperparameters; and 3. Deploy the trained
CNN models on the actual observational data to derive the output spectroscopic
parameters.
