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Solve Seq2Seq and Classification NLP tasks with Transformer and CNN using Tensorflow 2 in Google Colab

Modern Natural Language Processing in Python Description

What you’ll learn in Modern Natural Language Processing in Python

  • Build a Transformer, new model created by Google, for any sequence to sequence task (e.g. a translator)

  • Build a CNN specialized in NLP for any classification task (e.g. sentimental analysis)

  • Write a custom training process for more advanced training methods in NLP

  • Create customs layers and models in TF 2.0 for specific NLP tasks

  • Use Google Colab and Tensorflow 2.0 for your AI implementations
  • Quasar V1: PWA (with Vue JS 2, Firebase, NodeJS & Express)
    Pick the best model for each NLP task

  • Understand how we get computers to give meaning to the human language

  • Create datasets for AI from those data

  • Clean text data

  • Understand why and how each of those models work

  • Understand everything about the attention mechanism, lying behind the newest and most powerful NLP algorithms

Requirements for Modern Natural Language Processing in Python

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