Identifying similarities between ragas in Hindustani music impacts tasks like music recommendation, music information retrieval and automatic analysis of large-scale musical content. Quantifying raga similarity becomes extremely challenging as it demands assimilation of both intrinsic (viz., notes, tempo) and extrinsic (viz. raga singingtime, emotions conveyed) properties of ragas. This paper introduces novel frameworks for quantifying similarities between ragas based on their melodic attributes alone, available in the form of bandish (composition) notation. Based on the hypothesis that notes in a particular raga are characterized by the company they keep, we design and train several deep recursive neural network variants with Long Short-term Memory (LSTM) units to learn distributed representations of notes in ragas from bandish notations. We refer to these distributed representations as note-embeddings. Note-embeddings, as we observe, capture a raga's identity, and thus the similarity between note-embeddings signifies the similarity between the ragas. Evaluations with perplexity measure and clustering based method show the performance improvement in identifying similarities using note-embeddings over n-gram and unidirectional LSTM baselines. While our metric may not capture similarity between ragas in their entirety, it could be quite useful in various computational music settings that heavily rely on melodic information.