In this course, created by The University Of Toronto, you get to learn about Artificial Neural Networks and how they're being made use of for Machine Learning, as and when applied to modeling language, speech and object recognition, human motion, and image segmentation, etc. The emphasize is made on both the basic algorithms as well as the practical tricks that are required to get them to work well.
This course contains the same content that was presented on Coursera beginning in the year 2013. It is not a continuation or update of the original course and for the new platform has further been adapted.
A friendly introduction to Echo State Networks:
So How Does This Course Actually Work?
Each course presents itself like an interactive textbook, that features pre-recorded videos, quizzes as well as projects.
2. Help from Your Peers:
An individual can Connect with many other learners and debate ideas, discuss course material, and also get help in order to master concepts.
Official recognition is earned for your work, and your success can be shared with friends, colleagues, as well as employers.
How Do You Know If This Course Is For You?
The course is well suited for an intermediate level learner who is comfortable with calculus as well as has experience with programming in Python.
What will You get On This course Once You Enroll?
Once you get this course, you will have access to all of the features and content required to earn a Course Certificate. And once completed, the electronic Certificate will be added to your Accomplishments page.
What Does The Syllabus Comprise Of?
This course, created by The University of Toronto, that is taught by Geoffrey Hinton, is a 16 Week course, that comprises of topics as mentioned below:
Introduction to the course - Machine Learning and Neural Nets
The Perceptron learning procedure
The back propagation learning procedure
Learning feature vectors for words
Object recognition with neural nets
Optimization: How to make the learning go faster
More recurrent neural networks
Combining multiple neural networks to improve generalization
Hopfield nets and Boltzmann machines
Restricted Boltzmann machines (RBMs)
Deep neural nets with generative pre-training
Modeling hierarchical structure with neural nets
Recent applications of deep neural nets.
So, When Can You Have Access To The Lectures And Assignments?
Once an individual enrols for a Certificate, access is provided to all videos, quizzes, and if applicable programming assignments as well. Once your session has begun, only then Peer review assignments can be submitted and reviewed. If you still choose to explore the course without purchasing, certain assignments might not be accessible to you.
What If The Learner Requires Additional Time To Complete The Course?
A requirement of additional time by an individual is not a problem as the schedules of the course are quite flexible, and course fee payments provide 180 days of full course access as well as Certificate eligibility. There are suggested deadlines for Self-paced courses but there is no penalization for missing deadlines as long as the Certificate is earned by the individual within 180 days.
Session-based courses, in order to stay on track, may require the meeting of deadlines; but if an individual falls behind, he/she can switch to a later session, and any work completed will be transferred with them.
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Video Source: Artificial Intelligence Courses