From Machine Learning to Deep Learning
A journey to explore how Deep Learning evolved from Machine Learning
Lectures -6
Duration -2 hours
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Course Description
Why this Course?
A lot of us might have experienced difficulty when relating Machine Learning and Deep Learning models. This course aims to answer usual doubts such as,
Why Deep Learning?
Why Neural Network performs better than Machine Learning models?
Deep Learning and Machine Learning are totally different technologies or are they much related?
How has Deep Learning evolved from Machine Learning?
What it Covers?
The course covers Machine Learning models such as Linear Regression, Perceptron, Logistic Regression, and a Deep Learning model Dense Neural Network.
The four chapters (videos) of the course deal with the adult life of a Legend named Mr. S and show how he used Machine Learning and Deep Learning models to solve interesting problems such as partying, dating, searching for a soulmate, and eventually marrying the suitable girl in his life.
Through the journey of Mr. S, you will finally get to know why Neural Network performs better & how Machine Learning and Deep Learning are related.
Videos contain interesting scenarios with simple numerical examples and explanations.
Who can opt for this Course?
This course will be highly useful for those individuals,
Who does/doesn't have a CS background and wants to understand Deep Learning technically without coding & too much mathematics?
Who is getting started with Machine Learning or Deep Learning?
Who seeks the answer: Why do Neural Networks perform better than Machine Learning models and how has Deep Learning evolved from Machine Learning?
Who does research AI and has fundamental doubts about the functionality of Neural Networks.
Goals
To teach the journey from Machine Learning to Deep Learning
To relate how Deep Learning and Neural Networks evolved from Machine Learning models
To understand the inner functionality and design choices of a neural network
Prerequisites
- The course does not need any programming or theoretical prerequisite. Anyone who already has knowledge on Machine Learning and Deep Learning can also take this course.
![From Machine Learning to Deep Learning From Machine Learning to Deep Learning](https://d3mxt5v3yxgcsr.cloudfront.net/courses/14172/course_14172_image.png)
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
1 Lectures
-
Introduction to the course 01:10 01:10
Chapter 1. Party All the Time
1 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Chapter 2. Date All the Time
1 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Chapter 3. Search for Soulmate
1 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Chapter 4. Mission Marriage
1 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Conclusion
1 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Instructor Details
![Sridhar Swaminathan](https://www.tutorialspoint.com/assets/profiles/591983/profile/200_4004379-1719475876.jpg)
Sridhar Swaminathan
About:
Dr. Sridhar Swaminathan is an Subject Matter Expert at L&T EduTech in Chennai. Formerly he was an Assistant Professor at the Department of Computer Science Engineering, Bennett University, India. He is a Consultant for Artificial Intelligence, Data Science and Personal Learning. He is also a YouTuber running a channel named Majaa Matrix to teach AI and Data Science technologies (62 hours content) in the Tamil language.
Activities:
Between 2018-2020, he was actively involved in the nation-wide Project Leading India AI lead by Dr. Deepak Garg. He has conducted 64 workshops (16/24 hours of theory+practical at each venue) on AI and Deep Learning, 61 sessions (2 hours each) in Faculty Development Programs and 11 webinars (1-2 hours each) to this date.
Experience:
Formerly, He worked as a Post Doctoral Fellow in Bennett University from June 2017 to December 2018. He worked as a Research Officer in the Multimedia University, Malaysia. He completed his Ph.D. in the Department of Computer Science, Bishop Heber College, India under the guidance of Dr. Rajkumar Kannan.
Interests:
He works mainly on Computer Vision, Deep Learning and Natural Language Processing research problems. His research interests include Video Summarization, Super-resolution, Personalized Multimedia Retrieval, Visual Saliency Detection, Object Detection & Tracking and Sentiment Analysis.
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