How Neural Networks Actually Work — A Thread for Curious Minds
Learn how neural networks work by understanding their foundation in linear equations and how they're stacked to create complex models, which is crucial for building and applying AI and ML solutions
- Start with the basic linear equation y = mx + c to understand the foundation of neural networks
- Stack multiple linear equations to create a simple neural network model
- Apply activation functions to introduce non-linearity in the model
- Build a multi-layer perceptron to demonstrate the power of neural networks
- Run simulations to visualize how neural networks learn and improve
Data scientists, AI engineers, and software engineers on a team can benefit from understanding the basics of neural networks to build and improve their machine learning models. This knowledge helps them collaborate effectively and make informed decisions about model architecture and training
💡 Neural networks are essentially stacked linear equations with activation functions, allowing them to learn and represent complex relationships
💡 Neural networks are built from simple linear equations! #AI #ML
Key Takeaways
Learn how neural networks work by understanding their foundation in linear equations and how they're stacked to create complex models, which is crucial for building and applying AI and ML solutions
DeepCamp AI