Modern NLP using Deep Learning

Neural machine translation (NMT), Text summarization, Question Answering, Chatbot
Free tutorial
Rating: 3.9 out of 5 (10 ratings)
2,005 students
37min of on-demand video
English [Auto]

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


  • Motivation
  • Interset
  • Mathematical approach


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.


  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.


  • Wikipedia

  • Coursera

Who this course is for:

  • Anyone intersted in NLP
  • Anyone intersted in AI


Deep RL researcher
Nitsan Soffair
  • 2.8 Instructor Rating
  • 41 Reviews
  • 6,895 Students
  • 4 Courses

Currently Deep RL researcher at BGU with Masters of CS at BGU.

My thesis topic is Single agent to multi agent (SA2MA) Deep MARL algorithm beats famoues WQMIX created by Shimon whiteson, Head of Waymo reasearch.

My main interest is AI, while I am very enthusiastic about the new research at NLP decided to start teaching as best way for learning.

I have 2 years experience of teaching-assistant at BGU, particularly in Reinforcement learning course.

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