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[New] Concise and Practical AI/ML
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Preface
Artificial Intelligence
Concepts
High-level Intelligence
Maths for ML
Calculus
Algebra
Machine Learning
History of ML
ML Models
ML Model is Better
How a Model Learns
Boosted vs Combinatory
Neuralnet
Neuron
Types of Neurons
Layers
Neuralnet Alphabet
Heuristic Hyperparams
Feedforward
Input Separation
Backprop
Activation Functions
Loss Functions
Gradient Descent
Optimizers
Techniques
Normalization
Regularization
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Drop-out Technique
Concatenation
Problems in Training
Overfitting & Underfitting
Explosion & Vanishing
Methods of ML
Supervised Learning
Regression
Classification
Reinforcement Learning
Concepts
Bellman Equation
Q-table
Q-network
Learning Tactics
Policy Network
Unsupervised Learning
Some Applications
Other Methods
Practical Cases
Ref & Glossary
Regularization

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Drop-out Technique

Drop-out technique is a regularization technique which set some output of layers to zeros to limit using the whole network when data are not complete, network generalizes better.

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