Practicum for Deep Neural Networks
In the last decade, Neural Networks (NN) have attracted a lot of research due to their immense application potential. Breakthroughs of Deep Learning in image classification, speech recognition, and other challenging areas have provided the best solutions to many problems and significantly advanced “state of the art” AI machines in their ability to learn from data.
This course will introduce students to fundamental concepts of Deep Neural Network (DNN) development. It will cover important algorithms and modelling used in the development of these networks. Utilizing rich sets of available NN models, we will perform detailed analysis of leading ML approaches and popular NN like CNN, FCNN, ImageNet, ResNet, and RNN. TensorFlow framework exercises will develop practical skills, allowing students to gain confidence in understanding what ML concepts mean in neural network spaces. By the end of this course you will have learned important deep learning concepts and skills to create and train neural networks for specific practical problems.
- You will understand deep neural network (DNN) architecture
- Learn how to build a NN using TensorFlow
- Train a simple deep NN using TensorFlow
- Apply convolutional neural network (CNN) in image classification and image segmentation
- Apply DNN in natural language processing
- Write applications in TensorFlow2 for practical neural networks
- Opens career opportunities in one of most desirable fields today for ML/AI curriculum
Course typically offered: Online during our Summer and Winter academic quarters.
Prerequisites: CSE-41287: Linear Algebra for Machine Learning and CSE-41305: Probability and Statistics for Deep Learning, or equivilent knowledge.
Next Steps: Upon completion, consider additional coursework in our specialized certificate in Machine Learning Methods to continue learning.
More Information: For more information about this course, please contact firstname.lastname@example.org.
Note: There is a $25 fee to access the AX Account for this course (access to execute scripts in a persistent environment).
Course Number: CSE-41311
Credit: 3.00 unit(s)
Related Certificate Programs: Machine Learning Methods
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1/10/2023 - 3/11/2023
This course is entirely web-based and to be completed asynchronously between the published course start and end dates. Synchronous attendance is NOT required.
You will have access to your online course on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.
Bilyana Aleksic is senior staff hardware engineer with more than 20 years of experience in complex chip SOC development. She received her master's in control systems from Faculty of Electrical Engineering University of Belgrade, former Yugoslavia, and was an honored student of Mathematical Gymnasium, winning many world Mathematical Olympiad Competitions. Aleksic spent 15 years at Canadian tech company ATI Technologies where she worked on implementation of graphics CPU chips. After she moved to California she spent 7 years in mobile chips implementation and verification methodology development. During the past year she shifted her focus to machine learning for medical applications.
No information available at this time.
No refunds after: 1/16/2023.
1/10/2023 - 3/11/2023
You will have access to your course materials on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.
There are no sections of this course currently scheduled. Please contact the Science & Technology department at 858-534-3229 or email@example.com for information about when this course will be offered again.