NLP Training Overview
This Practical NLP (Natural Language Processing) training course teaches software engineers and data scientists how to incorporate NLP into their production systems. Participants learn the foundations for each NLP concept and explore each concept’s applicability, limitations, and implementations. Throughout this NLP course, both models and theory are taught using real-world datasets and production samples.
Location and Pricing
This course is taught as a private, live online class for teams of 3 or more. All our courses are hands-on, instructor-led, and tailored to fit your group’s goals and needs. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for online corporate training, please contact us.
In addition, some courses are available as live, instructor-led training from one of our partners.
Objectives
- Demonstrate popular NLP algorithms and their applicability and limitations
- Create production systems that process text efficiently
Prerequisites
Students should have some experience with machine learning. Familiarity with TensorFlow, Keras, and scikit-learn is helpful but not mandatory.
Outline
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Introduction
Representing Text for Similarity
- Word and Character Embeddings
- Keras Embedding Layers
- Word2Vec, CBOW, and Skip-Gram Architecture
- Doc2Vec and Paragraph Vectors. PV-DM and PV-DBOW Architectures
- Ranking documents
- Capstone: Learning to rank
Classification in Text
- Neural Networks
- Backpropagation through time
- Capstone: Sentiment Analysis
Generating Text for Suggestions
- Recurrent Neural Networks
- GRU and LSTM for memory retention
- Character and word-level text generation
- Autocomplete and Autosuggestions
- Capstone: Writing like Shakespeare
Named Entity Recognition (NER) Extracting Knowledge
- SpaCy and NER Pipelines
- Bidirectional LSTM networks
- Capstone: Translating
Language Modeling with Attention
- Self-Attention and “Hey Siri”
- Transformers
- Using BERT and GPT-2
- Knowledge Distillation of language models
- Capstone: Language models in proteins
Hackathon
- Distilling BERT for Detecting Toxic Comments
Conclusion
Training Materials
All NLP training students receive comprehensive courseware.
Software Requirements
- Python 3.5 or later
- TensorFlow 2.0 and Keras 2.0 or later within Google Collaboratory
