Machine Learning
2024
9
- Additional Deep Learning Topics: Transfer Learning, Model Compression, and Anomaly Detection Generative and Diffusion Models: GANs, Conditional Generation, and Diffusion Training Self-Attention and Transformer Architecture: BERT, GPT, and Multi-Head Attention
- Deep Learning Network Architectures: CNNs, RNNs, and Seq2Seq
- Text Embedding: From Bag-of-Words to Qwen3 Embedding
- Deep Learning Basics: Neural Networks, Optimization, and Normalization
- Interpretable Machine Learning: Model Explanations, SHAP, and Counterfactual Methods
- Advanced Machine Learning: Unsupervised and Semi-Supervised Learning
- Machine Learning Introduction: Supervised Learning and Bayesian Methods