Deep Learning Essentials
Master fundamental concepts of deep learning, from perceptrons to neural networks, with hands-on Python programming practice.
Master fundamental concepts of deep learning, from perceptrons to neural networks, with hands-on Python programming practice.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full AI and Machine Learning Essentials with Python Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
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What you'll learn
Understand the history and evolution of deep learning and AI
Master key concepts of neural networks and perceptrons
Implement deep learning models using Python and PyTorch
Apply backpropagation and gradient descent techniques
Develop practical skills through hands-on programming assignments
Skills you'll gain
This course includes:
3.5 Hours PreRecorded video
12 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course explores the fundamentals of deep learning, starting with the historical context and evolution of artificial intelligence. Students learn essential concepts including perceptrons, neural networks, and backpropagation through both theoretical understanding and practical implementation. The curriculum covers key topics such as stochastic gradient descent, kernel methods, and fully connected networks. Through hands-on programming assignments in Python and PyTorch, learners develop practical skills in implementing deep learning models while understanding the underlying mathematical principles.
History of Deep Learning
Module 1 · 3 Hours to complete
Perceptron, Stochastic Gradient Descent & Kernel Methods
Module 2 · 5 Hours to complete
Fully Connected Networks
Module 3 · 2 Hours to complete
Backpropagation
Module 4 · 4 Hours to complete
Fee Structure
Instructors
Leading Scholar in Natural Language Processing and Crowdsourcing
Chris Callison-Burch is an associate professor of Computer and Information Science at the University of Pennsylvania, where his work has positioned him as a thought leader in natural language processing (NLP) and crowdsourcing. Before joining Penn, he was a research faculty member at the Center for Language and Speech Processing at Johns Hopkins University, contributing significantly to advancements in the field during his six-year tenure.Chris has held prominent roles in major conferences and organizations, including serving as the General Chair of the ACL 2017 conference, Program Co-Chair for EMNLP 2015, Chair of the Executive Board of NAACL (2011–2013), and Secretary-Treasurer for SIGDAT (2015–2017). His editorial contributions span leading journals such as Transactions of the ACL (TACL) and Computational Linguistics.With over 100 publications that have been cited more than 10,000 times, Chris's research has had a profound impact on computational linguistics. Recognized as a Sloan Research Fellow, he has received prestigious faculty research awards from Google, Microsoft, Amazon, and Facebook. His work has also been supported by DARPA and the National Science Foundation (NSF).Chris's research interests focus on the intersections of natural language processing, machine translation, and the innovative use of crowdsourcing to tackle complex computational challenges. His contributions continue to shape the future of AI-driven language technologies and their practical applications.
Assistant Professor
Pratik Chaudhari is an Assistant Professor at the University of Pennsylvania, specializing in the theoretical and practical aspects of deep learning. His research focuses on understanding the mathematical underpinnings of deep learning algorithms and designing innovative solutions to improve their efficiency and robustness. He is particularly interested in optimization, generalization, and the intersection of machine learning with physics and engineering.With a strong emphasis on practical application, Pratik's work bridges the gap between theoretical advancements and real-world implementation, enabling better performance in diverse domains such as healthcare, robotics, and autonomous systems. He is deeply committed to mentoring students and fostering innovation in the field of artificial intelligence.
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