In the recent past, there has been interest in characterizing the physical and social ambiance of urban spaces to understand how people perceive and form impressions of these environments based on physical and psychological constructs. Building on our earlier work on characterizing ambiance of indoor places, we present a methodology to automatically infer impressions of place ambiance, using generic deep learning features extracted from images publicly shared on Foursquare. We base our methodology on a corpus of 45,000 images from 300 popular places in six cities on Foursquare. Our results indicate the feasibility to automatically infer place ambiance with a maximum R-2 of 0.53 using features extracted from a pre-trained convolutional neural network. We found that features extracted from deep learning with convolutional nets consistently outperformed individual and combinations of several low-level image features (including Color, GIST, HOG and LBP) to infer all the studied 13 ambiance dimensions. Our work constitutes a first study to automatically infer ambiance impressions of indoor places from deep features learned from images shared on social media.