Getting Started with Neural Networks: Biological & Artificial Neural Networks
Neural Networks
| Intermediate
- 12 Videos | 1h 3m 13s
- Includes Assessment
- Earns a Badge
Learners can explore fundamental concepts of biological and artificial neural networks, computational models that can be implemented with neural networks, and how to implement neural networks with Python, in this 12-video course. Begin with a look at characteristics of machine learning biological neural networks that inspired artificial neural networks. Then explore components of biological neural networks and the signal processing mechanism. Next, take a look at the essential components of the structure of artificial neural networks; learn to recognize the layered architecture of neural networks; and observe how to classify various computational models that can be implemented by using neural networks paradigm. Examine neurons connectivity, by describing the interconnection between neurons involving weights and fixed weights. This leads on to threshold functions in neural networks and the basic logic gates of AND, OR, and XNOR. Implement neural networks by using Python and the core libraries provided by Python for neural networks; create a neural network model using Python, Keras, and TensorFlow, and finally, view prominent neural network use cases. The concluding exercise involves implementing neural networks.
WHAT YOU WILL LEARN
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discover the key concepts covered in this course
identify the characteristics of biological neural networks that inspired artificial neural networks
list the essential components of biological neural networks and describe the signal processing mechanism of biological neural networks
describe essential components of artificial neural networks and their capabilities
recognize layered architectural patterns that can be used to implement neural networks
classify the various computational models that can be implemented using the neural networks paradigm -
describe the interconnection between neurons involving weights and fixed weights
describe threshold functions and the basic logic gates of AND, OR, and XNOR
implement neural networks using Python and the core libraries provided by Python for neural networks
create a neural network model using Python, Keras, and TensorFlow
list prominent use cases of implementing neural networks
recall the essential components of artificial neural networks, list the prominent use cases of neural networks, and implement neural networks using Python
IN THIS COURSE
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1.Course Overview1m 21sUP NEXT
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2.Neural Network Fundamentals6m 1s
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3.Biological Neural Network4m 6s
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4.Artificial Neural Network Structure4m 55s
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5.Neural Network Architecture4m 18s
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6.Computational Models in Neural Networks4m 39s
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7.Neurons Interconnection3m 57s
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8.Threshold Functions and Artificial Neural Networks3m 53s
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9.Implementing Neural Networks7m 4s
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10.Building Neural Network Models5m 15s
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11.Use Cases of Artificial Neural Network5m 16s
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12.Exercise: Implement Neural Networks7m 27s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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