The oceans mitigate climate change by absorbing 25–30 % of anthropogenic carbon emissions. Decadal variability in the ocean carbon sink has been suggested by pCO₂-based reconstructions of the ocean uptake, but these variations are less apparent in reconstructions based on global ocean biogeochemistry models (GOBMs) which also predict a slower increase in the ocean sink over the period from 2000 to 2015, raising concerns about our ability to accurately project future changes. An independent method to study the uptake uses interior observations to calculate how dissolved inorganic carbon (DIC) is changing. To address the sparsity of interior data, machine learning techniques have been applied, but as yet, global, full-depth reconstructions of the complete inventory of DIC have not been produced. Here we develop such a full-depth reconstruction, from the 1990s to 2019, using "ResNet-DIC", a deep neural network trained on GLODAPv2.2023 observations. Atmospheric CO₂, location, temperature, and salinity from EN4 analysis are used as predictors. Our method shows good predictive power when validated using independent data sets and model subsampling experiments. We diagnose the total oceanic DIC pool as 37,508 ± 274 Pg C in 2019 with an average rate of increase of 2.64 ± 0.13 Pg C/year over the whole time period. The global change in the DIC inventory exhibits pronounced peaks in decadal variability, especially in the early 2000s, driven primarily by intermediate waters at depths of 300–1000 m, particularly in the Atlantic, Indian, and Southern Oceans, and to a lesser extent in the Pacific. The accumulation rate of DIC increases steadily from the mid-2000s.