Neural Network Mathematics: Understanding the Mathematics of a Neuron

Math    |    Intermediate
  • 7 Videos | 50m 16s
  • Includes Assessment
  • Earns a Badge
First conceived in the 1940s, it wasn't until the early 2010s that artificial neurons showed their true potential as layered entities in the form of neural networks. When big data processing using distributed computing became mainstream, the computational capacity was now available to train these neural networks on huge datasets. Knowing this is one thing, but understanding how it all works is where the true potential lies. Use this course to gain an intuitive understanding of how neural networks work. Explore the mathematical operations performed by a single neuron. Recognize the potential of thousands of neurons connected together in a well-architected design. Finally, implement code to mathematically perform the operations in a single layer of neurons working on batch input. When you're finished, you'll have a solid grasp of the mechanisms behind neural networks and the math behind neurons.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    recall the architecture and components that make up neural networks
    summarize the mathematical operation of a neuron
    install Python modules
  • compute the weighted sum of inputs with bias
    process data in batches and with multiple layers
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    2m 4s
    UP NEXT
  • Playable
    2. 
    The Architecture and Components of Neural Networks
    10m 33s
  • Locked
    3. 
    The Math behind Neurons
    11m 6s
  • Locked
    4. 
    Installing Python Modules
    4m 2s
  • Locked
    5. 
    Performing Linear Transformation
    10m 37s
  • Locked
    6. 
    Processing Data in Batches
    10m 14s
  • Locked
    7. 
    Course Summary
    1m 40s

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