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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1.Course Overview1m 59sUP NEXT
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2.Online and Offline Learning6m 15s
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3.Training Patterns and Teaching Input8m 42s
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4.Training Samples9m 3s
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5.Baseline Overfitting and Underfitting9m 17s
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6.L1 and L2 Regularization6m 2s
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7.Training Neural Networks5m 24s
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8.Pattern Association Training Algorithms5m 13s
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9.Learning Vector Quantization7m 39s
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10.Modified Hebbian Learning4m 57s
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11.Hebbian Learning Rule5m 45s
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12.Competitive Learning7m 43s
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13.Optimizing Neural Networks7m 40s
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14.Debugging Neural Networks7m 13s
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15.Exercise: Implement Advanced Algorithms7m 44s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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