Improving Neural Networks: Data Scaling & Regularization
Neural Networks
| Intermediate
- 10 videos | 1h 37m 10s
- Includes Assessment
- Earns a Badge
Explore how to create and optimize machine learning neural network models, scaling data, batch normalization, and internal covariate shift. Learners will discover the learning rate adaptation schedule, batch normalization, and using L1 and L2 regularization to manage overfitting problems. Key concepts covered in this 10-video course include the approach of creating deep learning network models, along with steps involved in optimizing networks, including deciding size and budget; how to implement the learning rate adaptation schedule in Keras by using SGD and specifying learning rate, epoch, and decay using Google Colab; and scaling data and the prominent data scaling methods, including data normalization and data standardization. Next, you will learn the concept of batch normalization and internal covariate shift; how to implement batch normalization using Python and TensorFlow; and the steps to implement L1 and L2 regularization to manage overfitting problems. Finally, observe how to implement gradient descent by using Python and the steps related to library import and data creation.
WHAT YOU WILL LEARN
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discover the key concepts covered in this course
describe the approach of creating deep learning network models along with the steps involved in optimizing the networks
implement the learning rate adaptation schedule in Keras using SGD and specifying learning rate, epoch and decay
describe the concept of scaling data and list the prominent data scaling methods
describe the concept of batch normalization and internal covariate shift -
demonstrate how to implement batch normalization using Python and TensorFlow
implement L1 regularization to manage overfitting problems
implement L2 regularization to manage overfitting problems
demonstrate how to implement gradient descent using Python
recall the prominent data scaling methods, implement L1 regularization and gradient descent using Python
IN THIS COURSE
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1m 16s
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7m 48sAfter completing this video, you will be able to describe the approach of creating deep learning network models along with the steps involved in optimizing the networks. FREE ACCESS
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3. Rate Adaption Schedule Implementation with Keras7m 22sIn this video, find out how to implement the learning rate adaptation schedule in Keras using SGD and specifying learning rate, epoch and decay. FREE ACCESS
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4. Scaling and Scaling Methods4m 36sUpon completion of this video, you will be able to describe the concept of scaling data and list the prominent data scaling methods. FREE ACCESS
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5. Batch Normalization and Internal Covariate Shift7m 9sUpon completion of this video, you will be able to describe the concept of batch normalization and how it reduces internal covariate shift. FREE ACCESS
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6. Implementing Batch Normalization8m 16sIn this video, you will learn how to implement batch normalization using Python and TensorFlow. FREE ACCESS
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7. Implementing L1 Regularization17m 56sIn this video, you will learn how to implement L1 regularization to manage overfitting issues. FREE ACCESS
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8. Implementing L2 Regularization9m 33sIn this video, find out how to implement L2 regularization to avoid overfitting problems. FREE ACCESS
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9. Implementing Gradient Descent13m 26sIn this video, you will learn how to implement gradient descent using Python. FREE ACCESS
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10. Exercise: L1 Regularization and Gradient Descent19m 48sAfter completing this video, you will be able to recall the prominent data scaling methods, implement L1 regularization and gradient descent, and use Python. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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