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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
Case Studies
Generative
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