Training Neural Networks: Advanced Learning Algorithms
Neural Networks
| Intermediate
- 15 videos | 1h 40m 36s
- Includes Assessment
- Earns a Badge
This 15-video course explores how to design advanced machine learning algorithms by using training patterns, pattern association, the Hebbian learning rule, and competitive learning. First, learners examine the concepts and characteristics of online and offline training techniques in implementing artificial neural networks, and different training patterns in teaching inputs that are used in implementing artificial neural networks. You will learn to manage training samples, and how to use Google Colab to implement overfitting and underfitting scenarios by using baseline models. You will examine regularization techniques to use in training artificial neural networks. This course then demonstrates how to train previously-built neural network models using Python, and the prominent training algorithms to implement pattern associations. Next, learn the architecture and algorithm associated with learning vector quantization; the essential phases involved in implementing Hebbian learning; how to implement Hebbian learning rule using Python; and the steps involved in implementing competitive learning. Finally, you will examine prominent techniques to use to optimize neural networks, and how to debug neural networks.
WHAT YOU WILL LEARN
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Identify the subject areas covered in this course
Describe features of online and offline training methods in artificial neural network
Describe the training patterns and teaching inputs that are used in artificial neural networks
Describe the approach of managing training samples
Implement overfitting and underfitting using baseline model
Describe the regularization techniques used in deep neural network
Train built models of neural networks using python to implement prediction with high accuracy
List the prominent training algorithms that are used for pattern association -
Describe the architecture along with the algorithm associated with learning vector quantization
Define the essential phases involved in implementing hebbian learning
Implement the hebbian learning rule using python
Describe the steps involved in implementing competitive learning
List approaches of optimizing neural networks
Debug neural networks
Recall the training algorithms used for pattern association, list the steps of implementing competitive learning, and implement the hebbian learning rule using python
IN THIS COURSE
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1m 59s
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6m 15sUpon completion of this video, you will be able to describe features of online and offline training methods for artificial neural networks. FREE ACCESS
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3. Training Patterns and Teaching Input8m 42sAfter completing this video, you will be able to describe the training patterns and teaching inputs used in artificial neural networks. FREE ACCESS
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4. Training Samples9m 3sUpon completion of this video, you will be able to describe the approach of managing training samples. FREE ACCESS
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5. Baseline Overfitting and Underfitting9m 17sIn this video, you will learn how to implement overfitting and underfitting using a baseline model. FREE ACCESS
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6. L1 and L2 Regularization6m 2sUpon completion of this video, you will be able to describe the regularization techniques used in deep neural networks. FREE ACCESS
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7. Training Neural Networks5m 24sIn this video, learn how to train models of neural networks using Python to implement prediction with high accuracy. FREE ACCESS
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8. Pattern Association Training Algorithms5m 13sUpon completion of this video, you will be able to list the prominent training algorithms that are used for pattern association. FREE ACCESS
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9. Learning Vector Quantization7m 39sAfter completing this video, you will be able to describe the architecture and the algorithm associated with learning vector quantization. FREE ACCESS
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10. Modified Hebbian Learning4m 57sIn this video, learn how to define the essential phases involved in implementing Hebbian learning. FREE ACCESS
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11. Hebbian Learning Rule5m 45sFind out how to implement the Hebbian learning rule using Python. FREE ACCESS
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12. Competitive Learning7m 43sUpon completion of this video, you will be able to describe the steps involved in implementing competitive learning. FREE ACCESS
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13. Optimizing Neural Networks7m 40sUpon completion of this video, you will be able to list approaches for optimizing neural networks. FREE ACCESS
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14. Debugging Neural Networks7m 13sIn this video, you will learn how to debug neural networks. FREE ACCESS
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15. Exercise: Implement Advanced Algorithms7m 44sUpon completion of this video, you will be able to recall the training algorithms used for pattern association, list the steps of implementing competitive learning, and implement the Hebbian learning rule using Python. FREE ACCESS
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