Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization Course Overview

Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization Course Overview

"Improving Deep Neural Networks: Hyperparamater Tuning, Regularization and Optimization" certification focuses on advanced strategies for enhancing the performance of artificial intelligence models. This involves optimizing hyperparameters, implementing regularization to prevent overfitting, and using such techniques as batch normalization and dropout for better results. Industries use these strategies to refine their deep learning models, enabling them to make more accurate predictions and boost efficiency. The certification demonstrates proficiency in these areas, offering potentially higher job prospects in AI-driven fields. It can be particularly beneficial for data scientists, machine learning engineers, and AI specialists.

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  • Live Online Training (Duration : 24 Hours)
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  • Live Online Training (Duration : 24 Hours)
  • Per Participant

♱ Excluding VAT/GST

Classroom Training price is on request

You can request classroom training in any city on any date by Requesting More Information

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Course Prerequisites

  • Basic Python skills and the idea of loops, data structures, if/else statements, etc.
  • Basic knowledge of Machine learning concepts, liners algebra, and deep learning

Target Audience for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization Certification Training

• Machine Learning aspirants
• AI technology enthusiasts
• Data Scientists
• Software Engineers/Developers
• AI Professionals
• Computer Science students
• Individuals interested in neural networks
• Research scholars in Machine Learning
• Tech start-up teams.
• IT professionals seeking to improve AI applications

Why Choose Koenig for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization Certification Training?

- Certified Instructors: Highly-qualified, industry-experienced trainers guide the learning process.
- Boost Your Career: This specific training helps in career progression in the field of neural networks.
- Customized Training Programs: Course curriculum designed based on individual learning needs.
- Destination Training: Opportunity to learn in different global locations.
- Affordable Pricing: Quality education provided at mastery level with competitive pricing.
- Top Training Institute: Recognized globally for excellent training services.
- Flexible Dates: Schedule training as per your convenience.
- Instructor-Led Online Training: Learn anytime, anywhere under expert-guided online sessions.
- Wide Range of Courses: Options to choose from diverse set of courses.
- Accredited Training: Certificates are globally accepted, adds credibility to your profile.

Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization Skills Measured

After completing the Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization certification training, an individual can acquire skills in identifying and applying the appropriate hyperparameter tuning strategy, understanding how to use batch normalization and dropout for regularization. They will also learn how to perform optimization algorithms such as Adam, RMSprop and mini-Batch gradient descent. The training will help them to improve their skills in building high performing deep learning models, debugging and resolving network issues.

Top Companies Hiring Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization Certified Professionals

Google, IBM, Microsoft, Apple, Amazon, and Facebook are some of the top companies that hire professionals certified in Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization. These companies value such professionals for their ability to develop, fine-tune, and optimize deep learning models, thereby enhancing the performance of AI applications.

Learning Objectives - What you will Learn in this Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization Course?

The learning objectives of this course include acquiring a deep understanding of major aspects that enhance the performance and generalization ability of deep neural networks. They encompass gaining expertise in identifying and optimizing hyperparameters to successfully train models. Furthermore, students will learn regularization techniques to minimize overfitting issues, like L1 and L2 regularization, dropout, and early stopping. They will also get insights into the fundamentals of optimization algorithms to speed up the learning process, including SGD, RMSprop, and Adam. Lastly, mastering batch normalization and initialization techniques to improve deep networks' performance is also a key learning objective.