Deep-Learning-Workshop

The repository contains python codes, which I have developed as the facilitator of the two consecutive Deep Learning Workshops (PART-I and PART-II) for the master's students of computer science, University of Windsor.

"Workshop on Deep Learning" facilitator Shaon Bhatta Shuvo, PhD Student, School of Computer Science, University of Windsor, 401 Sunset Avenue, Windsor, Ontario, Canada. Email: [email protected]

Objective:

With the availability of data and the increase of computational power, the applications of deep learning and the popularity is increasing exponentially. The main objective of this workshop is to provide introductory theoretical knowledge on deep learning and practical implementations using python programming language and associated packages. Therefore, participants will get an overall idea of deep learning and its applications. This workshop will also guide students on what they need to learn to able to build their careers in this field. Overall the workshop shall work as a useful starting point and will encourage participants to dig deeper into the field of deep learning. Intended Learning Outcomes:

At the end of this workshop, active participants are expected to be able to:

1.	Identify the importance and applications of deep learning. 
2.	Choose between machine learning and deep learning approaches to solve a particular problem.
3.	Demonstrate basic concepts of deep learning workflow. 
4.	Build deep learning models from scratch. 
5.	Evaluate the model’s performance. 
6.	Demonstrate fundamental concepts of some state-of-the-art deep learning algorithmic approaches and implement those to different datasets. 
7.	Tune parameters to optimize the model’s performance. 
8.	Identify what to learn to become an expert in this field. 

Prerequisites:

1.	The participants are expected to have a minimum basic understanding of python programming language. 
2.	Basic knowledge of Machine Learning will help to understand the concept with less effort.  

Required Tools/Setup:

To follow the implantations along with the instructor and to avoid any unwanted delay, the participants are advised to install following tools/packages before joining the workshop:

1. Strongly Recommend:
   • Python (Latest Version)
   • Anaconda (Latest Version)
2. Better to have the latest version of the following packages installed to avoid unnecessary delay/interruption amid the workshop:
   • tensorflow
   • keras
   • numpy
   • pandas
   • matplotlib
   • scikit-learn
   • mlxtend

N.B: If any participant does not have the above setup, they can also use Google Colab to run the codes. However, the cloud service may not support a few packages (e.g., mlxtend).