Spaces of more than three dimensions are difficult to visualize. Those numbers locate each word as a point in 500-dimensional vectorspace. Just as Van Gogh's painting of sunflowers is a two-dimensional mixture of oil on canvas that represents vegetable matter in a three-dimensional space in Paris in the late 1880s, so 500 numbers arranged in a vector can represent a word or group of words. The vectors of words judged similar by their context are nudged closer together by adjusting the numbers in the vector. Each word's context in the corpus is the teacher sending error signals back to adjust the feature vector. When the feature vector assigned to a word cannot be used to accurately predict that word's context, the components of the vector are adjusted.

We use the latter method because it produces more accurate results on large datasets. It does so in one of two ways, either using context to predict a target word (a method known as continuous bag of words, or CBOW), or using a word to predict a target context, which is called skip-gram. Word2vec is similar to an autoencoder, encoding each word in a vector, but rather than training against the input words through reconstruction word2vec trains words against other words that neighbor them in the input corpus. It's a simple, yet unlikely, translation. So a neural word embedding represents a word with numbers. As Elvis Costello said: 'Writing about music is like dancing about architecture.' Word2vec 'vectorizes' about words, and by doing so it makes natural language computer-readable - we can start to perform powerful mathematical operations on words to detect their similarities. One thing describes another, even though those two things are radically different. The vectors we use to represent words are called neural word embeddings, and representations are strange.

The nations of Scandinavia and several wealthy, northern European, Germanic countries are among the top nine. Here's a list of words associated with 'Sweden' using Word2vec, in order of proximity: Sweden equals Sweden, while Norway has a cosine distance of 0.760124 from Sweden, the highest of any other country. Measuring cosine similarity, no similarity is expressed as a 90 degree angle, while total similarity of 1 is a 0 degree angle, complete overlap i.e. The output of the Word2vec neural net is a vocabulary in which each item has a vector attached to it, which can be fed into a deep-learning net or simply queried to detect relationships between words. Those clusters can form the basis of search, sentiment analysis and recommendations in such diverse fields as scientific research, legal discovery, e-commerce and customer relationship management. 'man' is to 'boy' what 'woman' is to 'girl'), or cluster documents and classify them by topic. Those guesses can be used to establish a word's association with other words (e.g. Given enough data, usage and contexts, Word2vec can make highly accurate guesses about a word’s meaning based on past appearances. Word2vec creates vectors that are distributed numerical representations of word features, features such as the context of individual words. That is, it detects similarities mathematically. The purpose and usefulness of Word2vec is to group the vectors of similar words together in vectorspace.

With that in mind, the tutorial below will help you understand how to create neural embeddings for any group of discrete and co-occurring states. So gene2vec, like2vec and follower2vec are all possible. Why? Because words are simply discrete states like the other data mentioned above, and we are simply looking for the transitional probabilities between those states: the likelihood that they will co-occur. It can be applied just as well to genes, code, likes, playlists, social media graphs and other verbal or symbolic series in which patterns may be discerned. Word2vec's applications extend beyond parsing sentences in the wild. Deeplearning4j implements a distributed form of Word2vec for Java and Scala, which works on Spark with GPUs. While Word2vec is not a deep neural network, it turns text into a numerical form that deep nets can understand. Its input is a text corpus and its output is a set of vectors: feature vectors for words in that corpus. Word2vec is a two-layer neural net that processes text.

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