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. Get state-of-the-art knowledge 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.

Resources

• Wikipedia
• Coursera

Who this course is for:

• Anyone intersted in NLP
• Anyone intersted in AI

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Course content

5 sections • 14 lectures • 37m total lengthCollapse all sections

Neural machine translation (NMT)5 lectures • 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 • 1min

• Transformer00:59
• Transformer3 questions

Question Answering2 lectures • 4min

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

Chatbot3 lectures • 5min

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

Bonus3 lectures • 20min

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

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