TensorFlow is an open source software library for numerical computation using data flow graphs. SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.
Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model. Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.
By attending Natural Language Processing (NLP) with TensorFlow workshop, delegates will:
- understand TensorFlow’s structure and deployment mechanisms
- be able to carry out installation / production environment / architecture tasks and configuration
- be able to assess code quality, perform debugging, monitoring
- be able to implement advanced production like training models, embedding terms, building graphs and logging
- Working knowledge of Tensorflow
- This Natural Language Processing (NLP) with TensorFlow training course is targeted at Developers and Engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.