Build & Train RNNs: Neural Network Components
Neural Networks
| Intermediate
- 10 videos | 36m 52s
- Includes Assessment
- Earns a Badge
Explore the concept of artificial neural networks (ANNs) and components of neural networks, and examine the concept of learning and training samples used in supervised, unsupervised, and reinforcement learning in this 10-video course. Other topics covered in this course include network topologies, neuron activation mechanism, training sets, pattern recognition, and the need for gradient optimization procedure for machine learning. You will begin the course with an overview of ANN and its components, then examine the artificial network topologies that implement feedforward, recurrent, and linked networks. Take a look at the activation mechanism for neural networks, and the prominent learning samples that can be applied in neural networks. Next, compare supervised learning samples, unsupervised learning samples, and reinforcement learning samples, and then view training samples and the approaches to building them. Explore training sets and pattern recognition and, in the final tutorial, examine the need for gradient optimization in neural networks. The exercise involves listing neural network components, activation functions, learning samples, and gradient descent optimization algorithms.
WHAT YOU WILL LEARN
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describe artificial neural network and its components
identify the topology of the networks that implements feedforward, recurrent and linked networks
list activation mechanisms used in the implementation of neural networks
specify the prominent learning samples that can be applied in neural networks
compare Supervised, Unsupervised, and Reinforcement learning samples -
describe training samples and the approaches for building them
identify training sets and recognize patterns
recognize the need for gradient optimization in neural networks
list neural network components, activation functions, learning samples, and gradient descent optimization algorithms
IN THIS COURSE
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1m 56s
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3m 23sAfter completing this video, you will be able to describe an artificial neural network and its components. FREE ACCESS
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3. Network Topologies7m 41sIn this video, find out how to identify the topology of the networks that implement feedforward, recurrent and linked networks. FREE ACCESS
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4. Neuron Activation Mechanism4m 18sAfter completing this video, you will be able to list activation mechanisms used in neural networks. FREE ACCESS
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5. Learning Samples2m 35sAfter completing this video, you will be able to specify the prominent learning samples that can be applied to neural networks. FREE ACCESS
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6. Supervised, Unsupervised, and Reinforcement3m 25sIn this video, find out how to compare Supervised, Unsupervised, and Reinforcement learning algorithms. FREE ACCESS
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7. Training Samples3m 1sUpon completion of this video, you will be able to describe training samples and the approaches for building them. FREE ACCESS
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8. Training Set and Pattern Recognition4m 17sIn this video, you will learn how to identify training sets and recognize patterns. FREE ACCESS
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9. Gradient Optimization Procedure4m 34sUpon completion of this video, you will be able to recognize the need for gradient optimization in neural networks. FREE ACCESS
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10. Exercise: Learning and Training Samples1m 42sAfter completing this video, you will be able to list neural network components, activation functions, learning samples, and gradient descent optimization algorithms. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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