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Deep Learning: Optimization & Fine-tuning
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Deep Learning: Optimization & Fine-tuning

Master deep learning optimization techniques, from regularization to hyperparameter tuning. Perfect for advancing your neural network expertise.

Course Cost

Free course

Intermediate

Skill Level

22 Hours

Self-paced lessons

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.

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4.9

5,69,213 Enrolled

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English

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olive-leaves-logo

4.9

5,69,213 Enrolled

olive-leaves-logo

English

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

Deep Learning
TensorFlow
Hyperparameter Tuning
Neural Networks
Regularization
Gradient Descent
Batch Normalization
Mathematical Optimization
Machine Learning
Python

This course includes:

5.4 Hours PreRecorded video

3 assignments

Access on Mobile, Tablet, Desktop

FullTime access

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Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.

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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

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Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

Deep Learning: Optimization & Fine-tuning

Intermediate

Skill Level

22 Hours

Self-paced lessons

Course Cost

Free course

Completion

CERTIFICATE

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.