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Natural Language Processing II

In this course, we will follow up on the basic Natural Language Processing course with more advanced topics.
Level
Designed for participants with basic knowledge and experience
intermediate
Course length
1 day
Language
 cz  eu
Course code
KT22110123
Machine learning
Category:
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Courses on a specific date with a live lecturer

Term
Language
Place
Form
?
How and where the course takes place.
Price without VAT
Open term
?
We will agree on a specific date together. This is a non-binding order.
Language
Place
online
Form
virtual classroom
?
Online training with a lecturer at a specific time.
Code of the course: KT22110123-0001
Price without VAT
4 990 Kč
Open term
?
We will agree on a specific date together. This is a non-binding order.
Language
Place
online
Form
virtual classroom
?
Online training with a lecturer at a specific time.
Code of the course: KT22110123-0002
Price without VAT
4 990 Kč

Course description

We will mainly focus on data preprocessing and the latest applications of deep learning in text processing. It will mainly be about the architecture of neural networks built on the so-called Transformers. Using the transfer learning method, we will show how large pre-trained neural networks can be used for various practical applications.

Required knowledge

  • Basic knowledge of Python programming
  • High School Mathematics
  • Course-level knowledge of machine learning Introduction to Machine Learning
  • Course-level knowledge Natural Language Processing

Course content

Preprocessing of text data
  • Character encoding and unicode normalization
  • Traditional tokenization (simple methods, Spacy, Moses)
  • Subword tokenization (byte-pair encoding, wordpiece, sentencepiece)
  • Data cleaning (deduplication, removal of text ballast)
Word embeddings
  • General principles
  • Implementation of the skip-gram model
Machine translation with recurrent networks
  • LSTM and GRU memory cells
  • Implementation of machine translation using recurrent networks
Transformers
  • Attention is all you need
  • Transformer architecture
  • GPT2
  • BERT
  • XLNET
Examples of transfer learning for natural language processing
  • Classification of texts
  • Recognizing named entities
  • Question answering

Lecturers

Jiří Materna
Jiří Materna

He is a machine learning specialist with experience in its applications in industry since 2007. Between 2008 and 2017, he worked at Seznam.cz, of which the last 7 years as head of the research department. He now works as a freelancer, offers the development of custom machine learning solutions, organizes the Machine Learning Prague conference and writes the ML Guru blog. 

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Previous courses

Do you want this tailor-made course for your company?

Contact us

News with the course

Náhledový obrázek novinky
Machine Learning 18. 3. 2023
The differences between Machine learning and Artificiant inteligence

Machine learning (ML) and Artificial intelligence (AI) are related fields, but they are not the same thing. AI is a broader field that encompasses many different technologies, including machine learning. Check with us the key differences between machine learning and artificial intelligence.

Náhledový obrázek novinky
Machine Learning 3. 6. 2021
Discover the benefits of Machine Learning

Machine Learning allows companies to be efficient, search for patterns in data, automate and make decisions with minimal human intervention. Learned algorithms solve defined tasks in real time and based on input data. At the same time, they learn from the new data and adapt to changing conditions.

Why with us