Deep Hybrid Neural Models with Optimizers - Analysis for Multiclass classification in NLP

Harsh Sharma (~harsh444)


In Recent Advances in NLP, Multi-class classification has become an important task. It is based on textual data refers to a supervised machine learning task, where we try to predict a certain class for input data, given the input data in raw textual format. Well, thanks to TensorFlow, we have a variety of algorithms available to perform multi-class classification via natural language processing.

For any deep learning enthusiast, it's always a tedious task to know, which type of individual neural network along with a correct set of loss functions and optimizers can lead to the best performance of their models. Furthermore, a computational constraint might also impact their choices for selecting a neural network. Hence, I have been doing detailed research analysis and comparison of various hybrid combinations of deep neural networks and choosing different optimizer's impact on their learning rates for models, choosing suitable callbacks in TensorFlow while training of models for their performance with total time taken to train them. Therefore, I would like to propose a talk on this topic, which could help a lot of developers to derive insightful results on these hybrid deep neural networks. I would be explaining why to go for hybrid models, instead of individual neural networks and which adaptive and non-adaptive algorithms to go for while choosing the appropriate deep neural network, time taken by a hybrid model combination to train on GPU and non-GPU environment, and also the impact of loss functions on those models. I would be explaining it with the results I obtained using the Tensorflow framework.

Talk Outline:

  • Short Intro About Me
  • Intro TO HYBRID NEURAL NETWORKS - Why hybrid neural networks?
  • Working of RNN AND CNN NETWORKS IN NLP VERY SHORT OVERVIEW OF THEIR WORKING- why they are better than individual Models
  • Basic Pipeline for hybrid models standard pipeline for deep hybrid neural nets
  • Different Model Inferences with Different Optimizers
  • Final Analysis, Metrics Evaluation, and Time Taken To Train hybrid models on GPU and Non-GPU Environment
  • Final Conclusion and Tips
  • Questionnaire Round


  • General Knowledge of Deep Learning and its basics [Activation Functions, Basic Optimizers as per Keras Documentation]
  • Basic Knowledge of Neural Networks like Convolutional Neural Networks [CNN], Long Short Term Memory Networks [LSTMs]
  • Basic Knowledge of Natural Language Processing [NLP] and experience with Natural Language Toolkit [NLTK] in terms of Deep Learning.
  • General Knowledge of Tensorflow Framework and Python.

Video URL:

Speaker Info:

Harsh Sharma is currently a CSE UnderGrad Student at SRM Institute of Science and Technology, Chennai. He is a Data Science Enthusiast and a passionate deep learning developer and researcher, who loves to work on projects belonging to Data Science Domain, By using a wide range of sources Available from Kaggle and other data-sharing platforms, to make some accessible models, by Applying appropriate analytical skills and algorithms. He has been shortlisted as finalists in quite a few hackathons and part of student-led tech clubs, handling executive positions after getting reviewed for his hard work, passion, and skills for deep learning. His contribution as a co-author in one of the research papers also got selected for IEEE-INCET 2021. Currently, he works mentoring juniors in hackathons,Hosting and organizing webinar sessions on deep learning, writing blogs based on deep learning-based natural language processing content, and collaborating with fellow deep learning developers on state-of-the-art projects and research titles constantly.

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Section: Data Science, Machine Learning and AI
Type: Talks
Target Audience: Intermediate
Last Updated: