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Modern NLP using Deep Learning

Neural machine translation (NMT), Text summarization, Question Answering, Chatbot

  • Free tutorial
  • Rating: 3.9 out of 53.9 (10 ratings)
  • 1,969 students
  • 37min of on-demand video
  • Created by Nitsan Soffair
  • English

What you’ll learn

  • Advance knowledge at modern NLP
  • Understand modern NLP techniques
  • Advance knowledge at modern DL
  • Understand modern DL techniques

Requirements

  • Motivation
  • Interset
  • Mathematical approach

Description

You will learn the newest state-of-the-art Natural language processing (NLP) Deep-learning approaches.

You will

  1. GetΒ state-of-the-art knowledgeΒ regarding
    1. NMT
    2. Text summarization
    3. QA
    4. Chatbot
  2. Validate your knowledge by answering short and very easy 3-question queezes of each lecture
  3. Be able to complete the course by ~2 hours.

Syllabus

  1. Neural machine translation (NMT)
    1. Seq2seq
      A family of machine learning approaches used for natural language processing.
    2. Attention
      A technique that mimics cognitive attention.
    3. NMT
      An approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modelling entire sentences in a single integrated model.
    4. Teacher-forcing
      An algorithm for training the weights of recurrent neural networks (RNNs).
    5. BLEU
      An algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
    6. Beam search
      A heuristic search algorithm that explores a graph by expanding the most promising node in a limited set.
  2. Text summarization
    1. Transformer
      A deep learning model that adopts the mechanism of self-attention, differentially weighting the significance of each part of the input data.
  3. Question Answering
    1. GPT-3
      An autoregressive language model that uses deep learning to produce human-like text.
    2. BERT
      A transformer-based machine learning technique for natural language processing (NLP) pre-training developed by Google.
  4. Chatbot
    1. LSH
      An algorithmic technique that hashes similar input items into the same “buckets” with high probability.
    2. RevNet
      A variant of ResNets where each layer’s activations can be reconstructed exactly from the next layer’s.
    3. Reformer
      Introduces two techniques to improve the efficiency of Transformers.
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Resources

  • Wikipedia
  • Coursera

Who this course is for:

  • Anyone intersted in NLP
  • Anyone intersted in AI

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Course content

5 sections β€’ 14 lectures β€’ 37m total lengthCollapse all sections

Neural machine translation (NMT)5 lectures β€’ 8min

  • Seq2seq01:41
  • Seq2seq3 questions
  • Attention01:36
  • attention3 questions
  • Neural machine translation (NMT)01:18
  • Neural machine translation (NMT)3 questions
  • BLEU01:54
  • BLEU3 questions
  • Beam search01:10
  • Beam search3 questions

Text summarization1 lecture β€’ 1min

  • Transformer00:59
  • Transformer3 questions

Question Answering2 lectures β€’ 4min

  • GPT-301:28
  • GPT-33 questions
  • BERT02:16
  • BERT3 questions

Chatbot3 lectures β€’ 5min

  • LSH01:21
  • LSH3 questions
  • RevNet01:59
  • RevNet3 questions
  • Reformer01:57
  • Reformer3 questions

Bonus3 lectures β€’ 20min

  • GPT-307:03
  • DALL-E05:09
  • CLIP07:37

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