Applied State of Art NLP (Using Deep Learning)

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Overview

With the advancements in machine learning and deep learning techniques, more and more use cases are being solved by various practitioners around the world. The problems which required human intervention in earlier days are now being solved by machines using latest advancements. This course brings the in depth knowledge about solving business use cases and hence helping business grow exponentially.

Objective

The course objective is to provide you an in depth understanding and hands on with current trends in Natural Language Processing.

Prerequisites

  • NLP basics
  • Calculus matrix & Basic Python
  • Deep Learning Knowledge is must

What you'll learn

  • Able to solve complex text data use cases which can vary from finding custom entities,
  • Developing human like conversation systems, analysing segments etc.
  • Implementation of Deep Learning Technique in Text
  • Fine tuning the pre-trained network

Course Outline

    • How deep learning was applied for texts?
    • Evolution of DL in texts
    • Introduction to famous NLP practitioners
    • Current Trends
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Gated Recurrent Units
  • Long short Term Memory Networks
  • Attention Mechanism
  • Transformers
  • Applying CNN for Text Classification
  • Applying CNN for Named entity Recognition
  • Applying RNN for Text Generation
  • Applying LSTM for Machine Translation
  • Applying LSTM for Chatbot development
  • Applying LSTM for classifying Names
  • Applying Attention Mechanism for Question and Answering Problems
  • Using Various Word embeddings to Hypertune above implementations
  • Transfer Learning in NLP
  • Building a Neural Network based conversational bot
  • Picking up Use cases from organisations to solve them
  • Research Paper discussions from Experts
  • Monthly interactions with Industry experts
  • Live Projects/Hackathons on request
  • Course Id                                           A104