Getting Started with Neural Networks: Biological & Artificial Neural Networks

Neural Networks    |    Intermediate
  • 12 Videos | 1h 3m 13s
  • Includes Assessment
  • Earns a Badge
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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

  • 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

  • Playable
    1. 
    Course Overview
    1m 21s
    UP NEXT
  • Playable
    2. 
    Neural Network Fundamentals
    6m 1s
  • Locked
    3. 
    Biological Neural Network
    4m 6s
  • Locked
    4. 
    Artificial Neural Network Structure
    4m 55s
  • Locked
    5. 
    Neural Network Architecture
    4m 18s
  • Locked
    6. 
    Computational Models in Neural Networks
    4m 39s
  • Locked
    7. 
    Neurons Interconnection
    3m 57s
  • Locked
    8. 
    Threshold Functions and Artificial Neural Networks
    3m 53s
  • Locked
    9. 
    Implementing Neural Networks
    7m 4s
  • Locked
    10. 
    Building Neural Network Models
    5m 15s
  • Locked
    11. 
    Use Cases of Artificial Neural Network
    5m 16s
  • Locked
    12. 
    Exercise: Implement Neural Networks
    7m 27s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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