Text Generation using Recurrent Neural Networks

Shashwat Aggarwal (~imshashwataggarwal)




Recurrent Neural Networks (RNNs) are the state of the art models that have shown great promise in many NLP tasks. There’s something magical about RNNs.

In the Talk, I will discuss the basic fundamentals of Recurrent Neural Networks, discuss the limitations of RNNs in generating texts and the problem of vanishing gradients. I will further discuss about LSTMs to overcome those problems and then train both character-level and word-level language models based on multi-layer LSTMs to generate text using Tensorflow and Numpy as dependancies.

The talk will be structured as below:

  • What are Recurrent Neural Networks(RNNs)?
  • Understanding the Vanishing Gradient Problem
  • Discussing and Implementing LSTM in Tensorflow
  • Training Character-level and Word-level language models on Harry Potter Corpus to generate some fun text following JK Rowling Style!


  • Python programming fundamentals.
  • Basics of Linear Algebra and Probability
  • Tensorflow
  • Basic Knowledge of Neural Networks

Content URLs:

Content for the session can be found here.

Speaker Info:

Hi! I am Shashwat Aggarwal, third year CS student at Netaji Subhas Institute of Technology.

I am very passionate about Machine Learning and Deep learning. I have been working on ML and DL technologies from past 6 - 7 months and had worked on numerous projects involving Data Mining, Image Processing, Machine Learning, Deep Learning and Natural Language Processing.

I have a keen interest in research activities and I have worked as a Research Intern at National Informatics Centre New Delhi during Summer on Boosting Algorithms in supervised domain of machine learning. I am an avid python user and as called by the community Pythonista .

Section: Data Analysis and Visualization
Type: Talks
Target Audience: Intermediate
Last Updated:

Hi can you please upload the slides for the talk so our team can review your proposal.

Pradhvan Bisht (~cyber_freak)

Yeah I will upload them ASAP.

Shashwat Aggarwal (~imshashwataggarwal)

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