Machine Learning Career Guide
0.1.0
  • Coding
  • Machine Learning Theory
    • Statistical Machine Learning
    • Deep Learning
      • The Fundamentals
        • The Big Picture
        • Gradient Descent
        • Backpropagation
        • Regularisation
      • Advanced Topics
      • Towards Theoretical Understanding of Representation Learning
      • Questions
    • Reinforcement Learning
  • Machine Learning Systems
  • Applied Machine Learning
  • Product System Design
  • General Skills
  • Non-Research Topics
Machine Learning Career Guide
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  • Machine Learning Theory »
  • Deep Learning »
  • The Fundamentals
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The FundamentalsΒΆ

  • The Big Picture
    • Probabilistic Framework
      • Defining the Probabilities
      • Usage of the Framework
      • Utilising the Observations
  • Gradient Descent
    • Nature of the Error Surface near Stationary Point
    • Gradient Descent
      • Batch Gradient Descent
      • Stochastic Gradient Descent
      • Mini-batch Gradient Descent
    • Convergence
      • Learning-Rate Schedule
      • Faster Covergence with Momentum
      • Adaptive Learning Rates
    • Managing Numerical Issues with Gradients
      • Weight & Bias Initialisation
      • Input normalisation
      • Weight normalisation
      • Resources
  • Backpropagation
    • Backprop Equations by Hand
      • Forward Pass
      • Backward Pass
    • AutoDiff
  • Regularisation
    • Inductive Bias
    • Weight Decay
      • Shrinkage
      • Consistent Reguraliser
    • Learning Curve
      • Early Stopping
      • Double Descent
    • Parameter Sharing
      • Hard-Sharing
      • Soft-Sharing
    • Residual Connections
    • Model Averaging
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