Deep Learning Literacy – Practical Application

Deep Learning Literacy - Practical Application


Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised

This path is focused on Deep Learning in action. We have pulled a series of examples to demonstrate how deep learning is embedded in our day to day lives. These are just in time sort of courses that reflect the journey from problem to solution.

The path is curated for Data enthusiasts that are eager to learn about Deep learning and foray into Data centered roles like Data scientist. Though this path will contain workable solutions, there is no requirement for the learner to have any background into Machine Learning or Deep learning. Intention is to have sandboxes for the path.

List of courses

  • Build a Model for Anomaly Detection in Time Series Data By Pratheerth Padman
  • Build a Rating Recommendation Engine with Collaborative Filtering By Pratheerth Padman
  • Build an Object Detection Model with Python By Gabrielle Davelaar
  • Deep Learning Application for Finance By Jaimin M
  • Deep Learning Application for Healthcare By Colin Matthews
  • Deep Learning Application for Marketing By Netta Tzin
  • Deep Learning Application for Retail By Trent McMillan
  • Implement Image Captioning with Recurrent Neural Networks By abdul-yousaf
  • Implement Image Recognition with a Convolutional Neural Network By Pratheerth Padman
  • Implement Text Auto Completion with LSTM By Biswanath Halder
  • Sentiment Classification with Recurrent Neural Networks By Biswanath Halder
  • Implement Natural Language Processing for Word Embedding By Axel Sirota


  • Understanding of algorithms used in the path. Though it is desired but not mandatory Understanding of Deep Learning Key concepts

Last Updated 12/2022

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