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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
Drop-out Technique
Concatenation
Problems in Training
Overfitting & Underfitting
Explosion & Vanishing
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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

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Methods of ML

Supervised Learning
Reinforcement Learning
Unsupervised Learning
Other Methods

Supervised Learning

Reinforcement Learning

Unsupervised Learning

Others

Incremental Learning

Transfer Learning




Supervised Learning
Reinforcement Learning
Unsupervised Learning
Others
Incremental Learning
Transfer Learning
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