- Maximizing the similarity of target words with actual context words.
- Minimizing the similarity of target words with 'k' negative context words or non-neighbor words

San Jose State University

From the given set of positive and negative training examples and initial weight for embeddings the goal of the learning algorithm is to learn to maximize the similarity of target word and context word from the positive examples and to minimize the similarity of target word and context word from the negative examples.

How to learn vectors

An on-going but a helpful book resource about NLP
https://web.stanford.edu/~jurafsky/slp3/

Speech and Language Processing (3rd ed. draft) 

Loss function for one w with cpos , cneg1 ...cnegk

A standard procedure for training deep neural networks involves optimizing the model's parameters. This is achieved by minimizing a loss function, which quantifies the model's error on the training data. An optimization algorithm, such as stochastic gradient descent, is used to iteratively adjust the parameters to reduce this loss.

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