Course details

Getting Started with Neural Networks: Biological & Artificial Neural Networks

Getting Started with Neural Networks: Biological & Artificial Neural Networks


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Explore the fundamental concepts of biological and artificial neural networks, computational models that can be implemented with neural networks, and how to implement neural networks using Python.



Expected Duration (hours)
1.0

Lesson Objectives

Getting Started with Neural Networks: Biological & Artificial Neural Networks

  • 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
  • Course Number:
    it_mlfdnndj_01_enus

    Expertise Level
    Intermediate