Neural Networks Basics

Notes from Session

• Neurons - Synapses. Model brain at high level
• Machine Learning  - Algorithms for classification and prediction
• Mimic brain structure in technology
• Recommender engines use neural networks
• With more data we can increase accuracy of models
• Linear Regression, y = mx + b. Fit data set with little error possible.

Neural Network

• Equation starts from neuron
• Multiply weights to inputs (Weights are coefficients)
• Apply activation function (Depends on problem being solved)

Basic Structure

• Input Layer
• Hidden Layer (Multiple hidden layers) - Computation done @ hidden layer
• Output Layer
• Supervised learning (Train & Test)
• Loss function determines how error looks like
• Deep Learning - Automatic Feature Detection

Happy Learning!!!