  We present a method for accelerating the calculation of CMB power spectra,
matter power spectra and likelihood functions for use in cosmological parameter
estimation. The algorithm, called CosmoNet, is based on training a multilayer
perceptron neural network and shares all the advantages of the recently
released Pico algorithm of Fendt & Wandelt, but has several additional benefits
in terms of simplicity, computational speed, memory requirements and ease of
training. We demonstrate the capabilities of CosmoNet by computing CMB power
spectra over a box in the parameter space of flat \Lambda CDM models containing
the 3\sigma WMAP1 confidence region. We also use CosmoNet to compute the WMAP3
likelihood for flat \Lambda CDM models and show that marginalised posteriors on
parameters derived are very similar to those obtained using CAMB and the WMAP3
code. We find that the average error in the power spectra is typically 2-3% of
cosmic variance, and that CosmoNet is \sim 7 \times 10^4 faster than CAMB (for
flat models) and \sim 6 \times 10^6 times faster than the official WMAP3
likelihood code. CosmoNet and an interface to CosmoMC are publically available
at www.mrao.cam.ac.uk/software/cosmonet.
