MACHINE LEARNING - IMPLEMENTATION NOTES

{ Approach | Algorithms | Advice ... }

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Preface

  1. Any thoughts/comments (added) are my own personal opinion.
  2. Note the mouse-over, scrollable Table of Contents, upper right corner (always visible)!
  3. These notes are less-formatted than my "parent" ML Notes   [<< large file; opens in new tab]. Basically I placed content here
    (reddit; ...) that I thought might be useful one day, without cluttering up that file.
  4. Enjoy!
Table of Contents 


General


ACTIVATION FUNCTIONS

Activation Functions - Blogs

Activation Functions - Instruction

Activation Functions - Papers


BATCH NORMALIZATION


CLASSIFICATION - DATA


CLASSIFICATION | CLUSTERING - IMAGES


CLASSIFICATION | CLUSTERING - TEXT


CLUSTERING


CNN - LAYERS, FILTERS


CNN - MISCELLANEOUS


CNN - POOLING


COST FUNCTIONS


CROSS-VALIDATION


DEBUGGING


ERROR


DEPTH


FEATURES-RELATED


GENERAL ADVICE - APPROACHES


GRADIENT BOOSTING


GRADIENT DESCENT


IMAGE CLASSIFICATION


IMAGE SEARCH


MEMORY-RELATED: GARBAGE COLLECTION ...


INITIALIZATION; PARAMETERS; ...


MEMORY-RELATED: LARGE DATA FILES


MISSING DATA, VALUES (NaN)


MODEL SIZES


OPTIMIZATION


OVERFITTING


PADDING


PARAMETERIZATION [PARAMETRIZATION | HYPERPARAMETERS]


PERCEPTRON


PRETRAINING


PROBABILITY (REGRESSION)


REGULARIZATION


REINFORCEMENT LEARNING { Q LEARNING | DQN }


ReLU [Rectified Linear Unit]


RNN [RECURRENT NEURAL NETS] | LSTM [LONG SHORT-TERM MEMORY]


SEMI-SUPERVISED LEARNING (SSL)


SUPERVISED LEARNING


TENSORFLOW


THEANO


TIME SERIES [e.g. AUDIO | VIDEO]


TRAINING | VALIDATION | TEST SETS


WEIGHT INITIALIZATION