Deep Learning: Optimization & Fine-tuning
Master deep learning optimization techniques, from regularization to hyperparameter tuning. Perfect for advancing your neural network expertise.
Master deep learning optimization techniques, from regularization to hyperparameter tuning. Perfect for advancing your neural network expertise.
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 Deep Learning 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.
4.9
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English
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What you'll learn
Master various initialization methods and regularization techniques
Implement advanced optimization algorithms including Adam and RMSprop
Apply batch normalization to improve neural network performance
Develop practical skills in TensorFlow implementation
Optimize hyperparameters for better model performance
Implement gradient checking for error detection
Skills you'll gain
This course includes:
5.4 Hours PreRecorded video
3 assignments
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FullTime access
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There are 3 modules in this course
This comprehensive course delves into the practical aspects of training deep neural networks effectively. Students learn essential techniques including initialization methods, regularization strategies, and optimization algorithms. The curriculum covers advanced concepts such as batch normalization, hyperparameter tuning, and gradient checking. Through hands-on programming assignments in TensorFlow, learners develop practical skills in implementing and optimizing neural networks while understanding the theoretical foundations behind these techniques.
Practical Aspects of Deep Learning
Module 1 · 12 Hours to complete
Optimization Algorithms
Module 2 · 5 Hours to complete
Hyperparameter Tuning, Batch Normalization and Programming Frameworks
Module 3 · 5 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
63,131 ratings
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