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Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

A highly practical course that strengthens your ability to fine-tune and optimize deep neural networks with cutting-edge techniques.

access

Lifetime

level

Medium

certificate

Certificate of completion

language

English

What will you learn in this Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization Course

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  • Master techniques to improve the training process of deep neural networks.

  • Learn how to perform effective hyperparameter tuning.

  • Understand and implement optimization algorithms like Adam and RMSprop.

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  • Apply dropout, batch normalization, and weight initialization to prevent overfitting.

  • Use TensorFlow to experiment with deep learning improvements.

Program Overview

1. Practical Aspects of Deep Learning
⏳  1 week
Focuses on challenges like vanishing gradients and overfitting. Teaches practical tips such as proper weight initialization, non-linear activation use, and effective training workflows.

2. Optimization Algorithms
⏳  1 week
Introduces algorithms such as mini-batch gradient descent, Momentum, RMSprop, and Adam. Covers learning rate decay and adaptive learning rates for training efficiency.

3. Hyperparameter Tuning and Batch Normalization
⏳  1 week
Covers techniques like random search, grid search, and use of TensorFlow for experimentation. Also dives into batch normalization and its benefits for faster convergence.

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Job Outlook

  • High demand for deep learning optimization skills in AI, robotics, and tech startups.

  • Opens roles like Machine Learning Engineer, Deep Learning Specialist, and AI Researcher.

  • Increases effectiveness in building high-performing, scalable AI models.

  • Supports freelance opportunities and R&D roles in cutting-edge AI projects.

9.7Expert Score
Highly Recommended
An in-depth course offering practical insights into optimizing deep neural networks, suitable for professionals aiming to enhance their deep learning expertise.
Value
9
Price
9.2
Skills
9.6
Information
9.7
PROS
  • Created by Andrew Ng and DeepLearning.AI.
  • Includes practical projects and real-world application tips.
  • Flexible learning for professionals.
  • Provides an industry-recognized certificate.
CONS
  • Assumes prior knowledge of neural networks and Python.
  • Some theoretical parts require a strong math background.

Specification: Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

access

Lifetime

level

Medium

certificate

Certificate of completion

language

English

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