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Deep Learning for Natural Language Processing
Level: Intermediate
Rating:
4.00/5 Based on 1 Reviews
Starting with the basics, this course teaches you how to choose from the various text pre- processing techniques and select the best model from the several neural network architectures for NLP issues.
Key Features of this Deep Learning for Natural Language Processing Course:
- After-course instructor coaching benefit
- After-course computing sandbox included
- Learning Tree end-of-course exam included
You Will Learn How To:
- Understand various pre-processing techniques for deep learning problems
- Build a vector representation of text using word2vec and GloVe
- Create a named entity recognizer and parts-of-speech tagger with Apache OpenNLP
- Build a machine translation model in Keras
- Develop a text generation application using LSTM
- Build a trigger word detection application using an attention model
Choose the Training Solution That Best Fits Your Individual Needs or Organizational Goals
LIVE, INSTRUCTOR-LED
In Class & Live, Online Training
- 3-day instructor led training course
- One-on-one after-course instructor coaching
- Pay later by invoice -OR- at the time of checkout by credit card
TRAINING AT YOUR SITE
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- Bring this or any training to your organization
- Full - scale program development
- Delivered when, where, and how you want it
- Blended learning models
- Tailored content
- Expert team coaching
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Our FlexVouchers help you lock in your training budgets without having to commit to a traditional 1 voucher = 1 course classroom-only attendance. FlexVouchers expand your purchasing power to modern blended solutions and services that are completely customizable. For details, please call 888-843-8733 or chat live.
In Class & Live, Online Training
Time Zone Legend:
Note: This course runs for 3 Days
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Feb 3 - 5 9:00 AM - 4:30 PM EST Online (AnyWare)
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Feb 24 - 26 9:00 AM - 4:30 PM EST Online (AnyWare)
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Mar 24 - 26 9:00 AM - 4:30 PM EDT Online (AnyWare)
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May 26 - 28 9:00 AM - 4:30 PM EDT Ottawa / Online (AnyWare)
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Sep 15 - 17 9:00 AM - 4:30 PM EDT New York / Online (AnyWare)
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Nov 22 - 24 9:00 AM - 4:30 PM EST Ottawa / Online (AnyWare)
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Guaranteed to Run
When you see the "Guaranteed to Run" icon next to a course event, you can rest assured that your course event — date, time — will run. Guaranteed.Important Deep Learning for Natural Language Processing Course Information
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Requirements
Strong working knowledge of Python, linear algebra, and machine learning is a must.
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Who Should Attend This Course
If you’re an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is just the book for you.
Deep Learning for Natural Language Processing Course Outline
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Lesson 1: Introduction to Natural Language Processing
- Basics of Natural Language Processing & application areas.
- Introduction to popular text pre-processing techniques.
- Introduction to word2vec and Glove word embeddings.
- Sentiment classification.
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Lesson 2: Applications of Natural Language Processing
- Introduction to Named Entity Recognition.
- Introduction to Parts of Speech Tagging.
- Using popular libraries to develop a Named Entity Recognizer.
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Lesson 3: Introduction to Neural Networks
- Introduction to Neural Networks.
- Basics of Gradient descent and backpropagation.
- What is Deep Learning.
- Introduction to Keras.
- Fundamentals of deploying a model as a service.
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Lesson 4: Foundations of Convolutional Neural Networks
- Introduction to CNN.
- Understanding the architecture of a CNN.
- Application areas of a CNN.
- Implementation using Keras.
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Lesson 5: Recurrent Neural Networks
- Introduction to RNN.
- Understanding the architecture of a RNN.
- Application areas of a RNN.
- Implementation using Keras.
- Vanishing Gradients with RNN.
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Lesson 6: Gated Recurrent Units
- Introduction to GRU.
- Understanding the architecture of a GRU.
- Application areas.
- Implementation using Keras.
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Lesson 7: Long Short Term Memory
- Introduction to LSTM.
- Understanding the architecture of an LSTM.
- Application areas.
- Implementation using Keras.
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Lesson 8: State of the art in Natural Language Processing
- Attention Model & Beam search.
- End to End models for speech processing.
- Dynamic Neural Networks for question answering.
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Lesson 9: A practical NLP project workflow in an organization
- Data acquisition (Free datasets, crowd-sourcing).
- Using cloud infrastructure to train deep learning NLP model (Google colab notebook).
- Writing a Flask framework server RestAPI to deploy a model.
- Deploy the web service on cloud infrastructure (AWS ec2 instance, docker).
- Current promising techniques in NLP (BERT and others).
Team Training
Deep Learning for Natural Language Processing FAQs
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Can I take this data science course online?
Yes! We know your busy work schedule may prevent you from getting to one of our classrooms which is why we offer convenient online training to meet your needs wherever you want, including online training.
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