Getting Started with Neural Networks: Biological & Artificial Neural Networks

Neural Networks    |    Intermediate
  • 12 videos | 58m 13s
  • Includes Assessment
  • Earns a Badge
Likes 32 Likes 32
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

  • 1m 21s
  • 6m 1s
    In this video, learn how to identify the characteristics of biological neural networks that inspired artificial neural networks. FREE ACCESS
  • Locked
    3.  Biological Neural Network
    4m 6s
    Upon completion of this video, you will be able to list the essential components of biological neural networks and describe the signal processing mechanism of biological neural networks. FREE ACCESS
  • Locked
    4.  Artificial Neural Network Structure
    4m 55s
    Upon completion of this video, you will be able to describe essential components of artificial neural networks and their capabilities. FREE ACCESS
  • Locked
    5.  Neural Network Architecture
    4m 18s
    Upon completion of this video, you will be able to recognize layered architectural patterns that can be used to implement neural networks. FREE ACCESS
  • Locked
    6.  Computational Models in Neural Networks
    4m 39s
    In this video, find out how to classify the various computational models that can be implemented using the neural networks paradigm. FREE ACCESS
  • Locked
    7.  Neurons Interconnection
    3m 57s
    After completing this video, you will be able to describe the interconnection between neurons involving weights and fixed weights. FREE ACCESS
  • Locked
    8.  Threshold Functions and Artificial Neural Networks
    3m 53s
    Upon completion of this video, you will be able to describe threshold functions and the basic logic gates of AND, OR, and XNOR. FREE ACCESS
  • Locked
    9.  Implementing Neural Networks
    7m 4s
    In this video, you will learn how to implement neural networks using Python and the core libraries provided by Python for neural networks. FREE ACCESS
  • Locked
    10.  Building Neural Network Models
    5m 15s
    In this video, you will learn how to create a neural network model using Python, Keras, and TensorFlow. FREE ACCESS
  • Locked
    11.  Use Cases of Artificial Neural Network
    5m 16s
    After completing this video, you will be able to list prominent neural network implementation use cases. FREE ACCESS
  • Locked
    12.  Exercise: Implement Neural Networks
    7m 27s
    After completing this video, you will be able to recall the essential components of artificial neural networks, list the prominent use cases of neural networks, and implement neural networks using Python. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.

Digital badges are yours to keep, forever.

YOU MIGHT ALSO LIKE

Course Convolutional Neural Networks: Implementing & Training
Likes 14 Likes 14  
Book Think AI: Explore the Flavours of Machine Learning, Neural Networks, Computer Vision and NLP with Powerful Python Libraries
Book Machine Learning with TensorFlow, Second Edition

PEOPLE WHO VIEWED THIS ALSO VIEWED THESE

Course Training Neural Networks: Advanced Learning Algorithms
Likes 12 Likes 12  
Course Clustering, Errors, & Validation
Likes 43 Likes 43  
Course Deep Learning & Neural Network Implementation
Likes 51 Likes 51