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Deep Learning

Updated May 04, 2023 ·

Overview​

Deep learning involves using algorithms called neural networks, which are inspired by how human brains work. These networks consist of interconnected nodes or neurons that process data and learn patterns.

  • Neurons, also known as nodes, are the basic units of these networks
  • Solves complex problems but requires large amounts of data
  • Best suited for less structured inputs like large texts or images

Case Study: Box Office Revenue​

Deep learning can be applied to real-world problems, like predicting the box office revenue of a movie based on various factors.

  • Simple Model Example

    • Predicting box office revenue based on production budget
    • A straight line through data points shows the relationship between budget and revenue
    • This is a prediction from a simple model
    • Neural network might use budget as input to predict revenue

  • Adding More Data

    • More information available: advertising spend, star power, timing of release
    • Leads to a more complex neural network

In a neural network, different neurons handle different aspects of the data.

  • First Neuron: Estimates spend based on budget and advertising costs
  • Second Neuron: Tracks awareness from advertising and star power
  • Third Neuron: Considers distribution decisions, budget, advertising, and release timing
  • Final Neuron: Takes outputs from previous neurons to estimate box office revenue

We can better understand this from the diagram below:

Training the Neural Network​

Training a neural network involves feeding it data and allowing it to learn the relationships between different inputs and outputs.

  • Training data is input to figure out relationships between neurons
  • Neural network learns by testing and analyzing these relationships

Deep Learning​

Deep learning involves using much larger neural networks, enabling the computation of very complex functions.

  • Real networks are much larger, with thousands of neurons

  • Stacking many neurons enables computation of complex functions

  • Provides accurate mappings from input to output

When to Use Deep Learning?​

Deep learning is powerful but should be chosen based on specific conditions.

  • Best for large datasets
  • Requires powerful computers for training
  • Excels in areas with lack of domain knowledge, as it identifies features for you
  • Shines in complex problems like computer vision and natural language processing