Semos Education Semos Education
  • Monday - Friday 9:00AM - 10:00PM
  • Call us now +44 7487633466
  • Keep in touch info@semosedu.com
EN / МК / RS
Кошничка
reserve a seat
  • Description
  • Content
  • Target Audience
  • Benefits
  • Certificates

Starting with the basics, this book 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.

 

Deep Learning for Natural Language Processing starts off by highlighting the basic building blocks of the natural language processing domain.

 

The course goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs.

 

As you advance through this deep learning course, you’ll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In the later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search.

  • Introduction to Natural Language Processing
  • Applications of Natural Language Processing
  • Introduction to Neural Networks
  • Foundations of Convolutional Neural Networks
  • Recurrent Neural Networks
  • Gated Recurrent Units
  • Long Short Term Memory
  • State of the art in Natural Language Processing
  • A practical NLP project workflow in an organization

If you are an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is the course for you.

 

A good working knowledge of Python, linear algebra and machine learning is a must.

By the end of this course, students will be able 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

Course attendance certificate issued by Semos Education

Description

Starting with the basics, this book 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.

 

Deep Learning for Natural Language Processing starts off by highlighting the basic building blocks of the natural language processing domain.

 

The course goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs.

 

As you advance through this deep learning course, you’ll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In the later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search.

Content
  • Introduction to Natural Language Processing
  • Applications of Natural Language Processing
  • Introduction to Neural Networks
  • Foundations of Convolutional Neural Networks
  • Recurrent Neural Networks
  • Gated Recurrent Units
  • Long Short Term Memory
  • State of the art in Natural Language Processing
  • A practical NLP project workflow in an organization
Target Audience

If you are an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is the course for you.

 

A good working knowledge of Python, linear algebra and machine learning is a must.

Benefits

By the end of this course, students will be able 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
Certificates

Course attendance certificate issued by Semos Education

Past experiences

What people say about us

  • - Borche Peltekovski Accredited Academy for Graphic Design

    After completing my studies at Semos Education, I envision myself working in a technology company, such as Samsung, Apple, or a company of similar caliber.

  • - Aleksandar Maksimov Student CertNexus Artificial Intelligence Academy

    Because Artificial Intelligence is the challenge of the future. With the modernization of lifestyles and technological advancements on a global scale, artificial intelligence is increasingly playing a key role in all aspects of life and development in society.

  • - Kristijan Stojoski Artificial Intelligence Academy

    With taking the first step and investing enough effort, everyone can master this modern topic and stand out in the job market in one of the fastest-growing industries in the world.

  • - Viktor Vanchov Artificial Intelligence Academy

    The final project taught me many useful things, beyond the realm of video games. However, it greatly helped me get an idea of how machines 'learn' and how powerful they can be.

Meet the instructors

  • Antonio Nikoloski AI Engineer @Pisstaccio, Software Developer @Asseco 2+ years of experience