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Concise and Practical AI/ML
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Concise and Practical AI/ML
Preface
What are AI and ML
Mathematics Recap
Calculus
Algebra
Libraries to Use
Models for ML
Methods of ML
Neuralnet Alphabet
Neuralnet
Neuron
Types of Neurons
Input Separations
Activation Functions
Layers in Network
Loss Functions
Gradient Descent
Feedforward
Backpropagation
Optimisers & Training
Techniques in ML
Normalisation
Regularisation
Concatenation
Boosted & Combinatory
Heuristic Hyperparams
Problems in Neuralnet
Overfitting
Explosion and Vanishing
Supervised Learning
Regression
Classification
Reinforcement Learning
Concepts
Learning Tactics
Policy Network
Bellman Equation
Q-table
Q-network
Unsupervised Learning
Some Applications
Incremental Learning
Case Studies
Algorithm Approximator
Regression
Classification
Sequence Learning
Pattern Learning
Generative
Notable Mentions
Neuralnet
Loss Functions
Common loss function are listed below. See the page
Neuralnet Alphabet
for notation and meanings of symbols. And
m
below is the number of neurons in output layer.
MAE
Mean Absolute Error
. This is not a good function, it’s not smooth.
MSE
Mean Squared Error
. This loss function is common and good for regression, but can be used in classification too.
CE
Cross-Entropy
. This loss function can work with both sigmoid and softmax activations.
For more maths, see:
https://gombru.github.io/2018/05/23/cross_entropy_loss/
CCE
Categorical Cross-Entropy
. This loss function is common and good for classification; this loss function should work with softmax activation only.
For more maths, see:
https://gombru.github.io/2018/05/23/cross_entropy_loss/
MAE
MSE
CE
CCE
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