Build & Train RNNs: Neural Network Components
Neural Networks
| Intermediate
- 10 Videos | 40m 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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1.Course Overview1m 56sUP NEXT
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2.Artificial Neural Network3m 23s
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3.Network Topologies7m 41s
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4.Neuron Activation Mechanism4m 18s
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5.Learning Samples2m 35s
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6.Supervised, Unsupervised, and Reinforcement3m 25s
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7.Training Samples3m 1s
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8.Training Set and Pattern Recognition4m 17s
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9.Gradient Optimization Procedure4m 34s
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10.Exercise: Learning and Training Samples1m 42s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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