"No one is harder on a talented person than the person themselves" - Linda Wilkinson ; "Trust your guts and don't follow the herd" ; "Validate direction not destination" ;

April 22, 2016

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!!!
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