Deep Learning – Natural Language Processing Training

Catalogue index: IFS/18

  • In stock
  • Delivery: free
  • Dedicated training:
  • Producer:
  • Training level:
    • Zaawansowany
  • Type of training:
    • stacjonarnie; online
PLN2,326.67 net / particip. PLN34,900.05  net for 1 workshop PLN2,861.80 gross incl. 23% VAT / particip.

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Features

Features
Certification:
  • TAK
Dedicated training:
Number of training hours:
  • 40
Producer:
Training language:
  • polski
Training level:
  • Zaawansowany
Type of training:
  • stacjonarnie; online

Description

Company Description

Infoshare is the largest tech community in CEE and the organizer of the leading tech conference in Gdańsk. It connects startups, investors, corporations, and innovation enthusiasts. It promotes entrepreneurship, knowledge sharing, and networking. Through events, competitions, and programs, it supports the development of the tech ecosystem in Poland and the region.

Training Description

Natural Language Processing (NLP) is one of the hottest areas of artificial intelligence (AI) thanks to applications such as text generators, chatbots, and programs that convert text to images. Recent years have shown an incredible development in computers' ability to understand human languages, programming languages, and even biological and chemical sequences, such as DNA and protein structures that resemble language. Many companies use NLP, including giants like Google, which uses NLP to improve search results, and Facebook, which uses NLP to detect and filter hate speech.

Who the Training is For
  • For programmers, data analysts, business analysts, marketers, designers, and anyone for whom machine learning significantly eases their work.
  • For those interested in technological innovations and the use of artificial intelligence in everyday work.
Goals
Benefits
  • How to prepare and analyze text data for machine learning (including tokenization, statistical analysis, building datasets)
  • How to create and train language models using recurrent networks in TensorFlow/Keras
  • How to use transformers and transfer learning for text classification
  • How to evaluate and improve the quality of NLP models through tuning and comparing their performance
Training Program

  1. INTRODUCTION

  2. TEXT DATA
     • Types of language models
     • Processing text data
     • Acquiring and reviewing the dataset
     • Building the dataset
     • Tokenization
     • Statistical analysis of the dataset
     • Preparing the dataset for modeling

  3. TEXT CLASSIFICATION WITH RECURRENT NETWORKS IN TF.KERAS
     • Theory of recurrent networks
     • Designing the model
     • Modeling
     • Qualitative analysis of the model
     • Tuning the model

  4. TRANSFORMERS
     • Theory of transformers
     • Transfer learning
     • Modeling
     • Comparing models

Duration

40 h/ 5 days

Price Includes

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