schedule Aug 10th 10:00 AM - 06:00 PM place Jupiter 2 people 76 Interested add_circle_outline Notify

Data that forms the basis of many of our daily activities like speech, text, videos has sequential/temporal dependencies. Traditional deep learning models, being inadequate to model this connectivity needed to be made recurrent before they brought technologies such as voice assistants (Alexa, Siri) or video based speech translation (Google Translate) to a practically usable form by reducing the Word Error Rate (WER) significantly. RNNs solve this problem by adding internal memory. The capacities of traditional neural networks are bolstered with this addition and the results outperform the conventional ML techniques wherever the temporal dynamics are more important.
In this full-day immersive workshop, participants will develop an intuition for sequence models through hands-on learning along with the mathematical premise of RNNs.

 
 

Outline/Structure of the Workshop

  1. Prerequisites Review
  2. Optimization Techniques in Deep Neural Network
  3. Building blocks of Artificial Neural Networks
  4. Back-propagation Algorithm
  5. Sequence Models
  6. Converting base models to recurrent form
  7. Case Study & Live Coding Exercise: Genomics
  8. Adding Long Term Memory to Recurrent Neural Nets via Gates: The GRU Model
  9. Selective Information Retention: The LSTM Model

Learning Outcome

Participants will

  1. Gain insight into the mathematical premise of sequence models
  2. Develop an intuitive understanding of sequence models.
  3. Know how to develop and apply sequence models for text, genomics and time-series data

Target Audience

Anyone who is interested in learning how to effectively gain insights in sequence data

Prerequisites for Attendees

Following instructions have been tested on Windows 10 and found to be working. YMMV on other OSs.

  1. Download and install Anaconda from here. Select the Python 3.7 version installer
  2. Open command line. Change to the directory 'condabin' where you installed Anaconda
    for ex. cd c:\foo\Anaconda3\condabin
  3. Execute the command conda install keras (make sure you are connected to the internet)
  4. Jupyter is available by default
    You can launch jupyter from Anaconda Navigator. But this opens it in the user directory by default so you will have to copy all code to the user directory.
    It is recommended that you launch jupyter from the directory wherever you clone the exercises. For this certain dlls have to be added to Path. Follow instructions in step 5 to get this working
  5. Neither jupyter nor conda is added to the path by default
    Add \Library\bin to environment variable 'Path'
    Add \condabin to environment variable 'Path'
  6. Now you can change to any directory on your machine and launch jupyter using command:
    jupyter notebook []

The hands-on exercises would be in python, so some familiarity with python would make it easier for the participants to move through the exercises.

Knowledge about ANN is recommended but not a necessity, we will give an overview of the relevant theory.

schedule Submitted 7 months ago

Public Feedback

comment Suggest improvements to the Speaker
  • Shahan Degamwala
    By Shahan Degamwala  ~  3 months ago
    reply Reply

    I like these aspects of the submission, and they should be retained:

    • ...

    I think the submission could be improved by:

    • ...Could you please confirm the packages that need to be pre-installed? 
    • Vishal Gokhale
      By Vishal Gokhale  ~  3 months ago
      reply Reply

      We'll update the list next week.

      Python, tensorflow, keras should be as stated in the proposal above.

       

      We would prefer Google colab, but it's subject to internet availability.

  • Dipanjan Sarkar
    By Dipanjan Sarkar  ~  6 months ago
    reply Reply
    • This looks very promising, how about changing the name to remove RNNs and give something more generic like sequential neural network models \ deep learning models or something which basically means you are covering all types of sequential NN models
    • Maybe you can also add in bi-directional GRUs\LSTMs since they have proven to work well these days specially w.r.t NLP problems also
    • Also maybe sequence-to-sequence (encoder-decoder models) like the ones used in neural machine translation
    • Vishal Gokhale
      By Vishal Gokhale  ~  6 months ago
      reply Reply

      Dear Dipanjan, thank you for your positive comment.

      We initially considered doing a workshop on all sequence modeling techniques, however decided against it. 
      From last year's workshops, we saw that attendees are at a broad range of expertise. And for a theory+hands-on session, it is really hard to cover multiple concepts in reasonable depth within the span of a day.

      Hence, we will be doing theory and hands-on for RNNs and cover LSTM, bi-directional LSTM and GRU at conceptual level (mentioned in the outline).
      But we didn't put it explicitly in the title because we thought that the title could be seen as misleading if we include those. 
      We don't want to over-commit and under-deliver. It would be rather good to do the other way around. Hope that explains our thinking. 

      Also, since there are quite a few proposals which are based on NLP, we thought we should rather focus on the techniques applied in other domains e.g. genomics. But encoder-decoder can certainly be added if that makes more sense.

       

      Thanks, 
      Rahee and Vishal

      • Sai Sundarakrishna
        By Sai Sundarakrishna  ~  3 months ago
        reply Reply

        Makes sense. Encoder decoder models with attention would be a great add


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