UsefulLinks
Computer Science
Artificial Intelligence
Generative AI
1. Introduction to Generative AI
2. Mathematical and Technical Foundations
3. Machine Learning Fundamentals
4. Core Generative Model Architectures
5. Transformer Architecture and Language Models
6. Text Generation Applications
7. Image and Visual Generation
8. Audio and Speech Generation
9. Development Lifecycle and Best Practices
10. Practical Implementation Tools
11. Ethical Considerations and Responsible AI
12. Legal and Regulatory Landscape
13. Economic and Social Impact
14. Future Directions and Emerging Trends
2.
Mathematical and Technical Foundations
2.1.
Probability and Statistics
2.1.1.
Probability Distributions
2.1.1.1.
Discrete Distributions
2.1.1.2.
Continuous Distributions
2.1.1.3.
Joint and Conditional Distributions
2.1.2.
Bayesian Inference
2.1.2.1.
Prior and Posterior Distributions
2.1.2.2.
Bayes' Theorem Applications
2.1.2.3.
Maximum Likelihood Estimation
2.1.3.
Information Theory
2.1.3.1.
Entropy and Cross-Entropy
2.1.3.2.
Mutual Information
2.1.3.3.
KL Divergence
2.2.
Linear Algebra for AI
2.2.1.
Vectors and Vector Spaces
2.2.1.1.
Vector Operations
2.2.1.2.
Dot Products and Norms
2.2.1.3.
Linear Independence
2.2.2.
Matrices and Matrix Operations
2.2.2.1.
Matrix Multiplication
2.2.2.2.
Eigenvalues and Eigenvectors
2.2.2.3.
Matrix Decomposition
2.2.3.
Tensors
2.2.3.1.
Tensor Operations
2.2.3.2.
Broadcasting
2.2.3.3.
Tensor Calculus
2.3.
Optimization Theory
2.3.1.
Gradient-Based Methods
2.3.1.1.
Gradient Descent Variants
2.3.1.2.
Stochastic Gradient Descent
2.3.1.3.
Momentum and Adaptive Methods
2.3.2.
Loss Functions
2.3.2.1.
Regression Losses
2.3.2.2.
Classification Losses
2.3.2.3.
Generative Model Losses
2.3.3.
Regularization Techniques
2.3.3.1.
L1 and L2 Regularization
2.3.3.2.
Dropout
2.3.3.3.
Batch Normalization
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1. Introduction to Generative AI
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3. Machine Learning Fundamentals