Neural Networks and Deep Learning
Master foundational concepts of neural networks and deep learning with hands-on Python implementation.
Master foundational concepts of neural networks and deep learning with hands-on Python implementation.
In this foundational course of the Deep Learning Specialization, students learn to build, train, and apply fully connected deep neural networks. The curriculum covers essential concepts from basic neural network architecture to advanced implementation techniques. Through hands-on programming assignments and practical examples, learners develop proficiency in implementing efficient neural networks, understanding key architectural parameters, and applying deep learning to real-world applications.
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What you'll learn
Build and implement neural networks from scratch
Understand the mathematics behind deep learning algorithms
Master vectorization for efficient neural network implementation
Apply deep learning to practical applications
Implement forward and backward propagation in neural networks
Skills you'll gain
This course includes:
6.78 Hours PreRecorded video
8 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course provides a strong foundation in neural networks and deep learning. Beginning with fundamental concepts, students progress through increasingly complex topics including binary classification, shallow neural networks, and deep neural architectures. The curriculum combines theoretical understanding with practical implementation, featuring programming assignments in Python and real-world applications. Each module builds upon previous knowledge, culminating in the ability to build and train deep neural networks for various applications.
Introduction to Deep Learning
Module 1 · 2 Hours to complete
Neural Networks Basics
Module 2 · 7 Hours to complete
Shallow Neural Networks
Module 3 · 5 Hours to complete
Deep Neural Networks
Module 4 · 8 Hours to complete
Fee Structure
Instructors
Pioneer in AI and Online Education
Andrew Ng is the Founder of DeepLearning.AI, a General Partner at AI Fund, and the Chairman and Co-Founder of Coursera, where he also serves as an Adjunct Professor at Stanford University. Renowned for his groundbreaking contributions to machine learning and online education, Dr. Ng has transformed countless lives through his work in AI, having authored or co-authored over 100 research papers in machine learning, robotics, and related fields. His notable past roles include serving as chief scientist at Baidu and leading the founding team of Google Brain. Currently, Dr. Ng focuses on his entrepreneurial ventures, seeking innovative ways to promote responsible AI practices across the global economy.
Stanford AI Educator Pioneers Global Learning Through Course Innovation and EdTech Leadership
Younes Bensouda Mourri is a distinguished AI educator and entrepreneur who has significantly impacted global tech education. Born and raised in Morocco, he earned his B.S. in Applied Mathematics and Computer Science and M.S. in Statistics from Stanford University, where he now teaches Artificial Intelligence both on campus and online. As the founder of LiveTech.AI, he develops AI tools to transform academic institutions, while his courses have reached over 1.3 million learners worldwide, with 23% securing AI-related jobs after completion. His contributions include co-creating Stanford's Applied Machine Learning, Deep Learning, and Teaching AI courses, as well as developing the highly successful Natural Language Processing Specialization for DeepLearning.AI. Starting as a teaching assistant in Andrew Ng's Machine Learning course, he rose to become an Adjunct Lecturer at Stanford by age 22, demonstrating his commitment to democratizing AI education. Through his work with major companies like ASML, CISCO, and Boston Consulting Group, he continues to advance AI education while focusing on developing innovative NLP tools for personalized feedback and chain-of-thought reasoning
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4.9 course rating
1,21,903 ratings
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