Deep Learning Engineering Essentials
Master deep learning algorithms and neural networks with hands-on TensorFlow projects in this advanced engineering course.
Master deep learning algorithms and neural networks with hands-on TensorFlow projects in this advanced engineering course.
This comprehensive 16-week course provides a deep dive into the fundamentals of deep learning for engineering applications. Students gain practical experience implementing neural networks using TensorFlow and Keras, while developing a thorough understanding of deep learning principles. The curriculum covers essential topics from basic feedforward networks to advanced architectures like CNNs and RNNs, with emphasis on optimization, regularization, and practical implementation.
Instructors:
English
English
Powered by
What you'll learn
Develop and justify state-of-the-art deep learning algorithms
Make informed design decisions in neural network architecture
Implement and optimize advanced deep neural networks
Address security concerns in deep learning systems
Explore current research problems and potential solutions
Skills you'll gain
This course includes:
Live video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
Closed caption
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 5 modules in this course
This intensive course covers fundamental concepts and practical implementation of deep learning algorithms. Starting with basic feedforward networks, students progress through advanced topics including regularization techniques, optimization methods, convolutional neural networks, and recurrent neural networks. The curriculum emphasizes hands-on experience with TensorFlow and Keras, enabling students to implement and optimize state-of-the-art neural network architectures.
Introduction to Deep Feedforward Networks
Module 1
Regularization for Deep Learning
Module 2
Optimization for Training Deep Models
Module 3
Convolutional Neural Networks
Module 4
Recurrent Neural Networks
Module 5
Fee Structure
Instructor
Distinguished Scholar Advancing Machine Learning and Information Theory
Aly El Gamal is an Assistant Professor in Electrical and Computer Engineering at Purdue University, bringing extensive expertise in machine learning and information theory. His academic journey includes a Ph.D. in Electrical and Computer Engineering and an M.S. in Mathematics from the University of Illinois at Urbana-Champaign, preceded by degrees from Nile University and Cairo University. His career includes valuable industry experience as an intern at Qualcomm's Office of the Chief Scientist and a postdoctoral position at the University of Southern California. El Gamal has earned numerous prestigious recognitions, including the 2021 Online Excellence in Innovative Course Design and Use of Technology award, multiple Purdue Engineering Outstanding Teacher Awards, and significant research grants from DARPA and AFRL. His research contributions span deep learning, wireless networks, and information theory, with over 35 journal papers and significant work in areas such as interference management and machine learning applications. He currently leads research initiatives in adaptive wireless networks and machine learning algorithms while serving as an associate editor for IEEE Transactions on Wireless Communications
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.