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 learn to:
- Understand TensorFlow’s structure and deployment mechanisms
- Carry out installation / production environment / architecture tasks and configuration
- Assess code quality, perform debugging, monitoring
- 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.