The School of Computer Science at the University of Windsor is pleased to present...
Technical Workshop Presentation by
Basics of Deep Learning using Python
Date: Friday, November 18, 2022
Time: 12:00 pm – 1:30 pm
Location: 4th Floor (Workshop space) at 300 Ouellette Avenue (School of Computer Science Advanced Computing Hub)
LATECOMERS WILL NOT BE ADMITTED once the presentation has begun.
Abstract:
With the availability of data and increased computational power, deep learning applications and their popularity are growing 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 build their careers in this field. Overall, the workshop shall work as a helpful starting point and will encourage participants to dig deeper into the field of deep learning.
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. Identify the use of the Training, Test and Validation data set.
5. Demonstrate the basic concept of different elements of deep Neural Networks, including Activation Function, Objective Function, Optimization Algorithms, Backpropagation etc.
6. Build deep learning models from scratch.
7. Evaluate the model’s performance.
8. Distinguish between Parameter and Hyperparameter.
9. Identify the importance of hyperparameter tuning.
Prerequisites:
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
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 the following
tools/packages before joining the workshop:
1. Strongly Recommend:
- 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
- MLX tend
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., MLX tend).
Biography:
Shaon is currently has a Ph.D. Candidate and Graduate Teaching Assistant in the School of Computer Science at the University of Windsor, Canada. Besides, I am also working as a Research Assistant and have already worked on various funded projects, including CIHR, NSERC, and Tire 1 Canada Research Chair program. I have skills in multiple fields of Artificial Intelligence, especially Machine Learning, Deep Learning and Agent-Based Modeling. I have research publications in journals and various top-tier conferences, including KDD and the best paper award in SSDBM’20. I also have more than five years of university-level teaching experience in multiple universities, including a recent sessional instructor position at the University of Windsor.