Getting Started with Deep Learning in Python

Iishi Patel (~iishipatel)




This will be an introductory session for python enthusiasts to dive deep into deep learning applications. As we know python is the most used and preferred language for cutting edge technologies like Artificial Intelligence, Machine Learning, and of course Deep Learning. Learning. This talk will focus on how to implement deep learning in real-time problems rather than knowing its theory. The talk will explore different python libraries used and the advantages and disadvantages of them. We will also look into a hands-on example of Artificial Neural Networks and loads of demos to create curiosity among attendees.

Outline of the sessions and topics covered:

  • Deep Learning and its Applications (5 mins)
  • Python Libraries: Tensorflow vs PyTorch( 2 mins)
  • Hands-on: ANN-based Project (15 mins)
  • Scope: NLP vs Computer Vision vs Voice Augmentation (5 mins)
  • Project Demonstration to ignite curiosity (3 mins)


  • Basic Python programming skills
  • OOPS in Python
  • Familiarity with data pre-processing libraries like Numpy, Pandas, Mathplotlib, etc.

Video URL:

Content URLs:

Speaker Info:

I am a budding Machine Learning Engineer, currently, in my 3rd year, I am studying at Vellore Institute of Technology. I am a proficient python developer and understand the language deeply. I have done several machine learning/ deep learning projects and you can find an insight into it on my Github. My latest and most celebrated project till now is working with autoencoders for denoising images. I have written a medium article on it too which is published under the reputed data science blog Towards Data Science. I have teaching and guiding skills in the field of data science, machine learning, deep learning, and backend development ( Django as well as Flask ) as being the Research Lead of ACM Student Chapter at Vellore Institute of Technology. Working with ACM I have conducted several workshops and seminars helping juniors and fellow mates getting started with machine learning and python based back end frameworks.

Speaker Links:

  • Profile:
  • GitHub:
  • Medium Article:
  • LinkedIn:

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