- So begins my documentation AI learning into the field of AI. AI is vast and continually evolving. This is my page to build a foundational understanding of AI with some deep dives into specific topics of interest.
NOTE: This is my personal learning path that I share for educational purposes.
- Google Colab - set open -> github -> jtstroup/ai_learner -> {branch name} and select any notebook
- Gradio
- History of AI
- Mathematical Foundations
- Word Embeddings
- Text Classification
- Language Modeling
- Seq2seq and Attention
- Sentiment Analysis
- Named Entity Recognition (NER)
- Topic Modeling
- Speech Recognition and Processing
- Natual Language Generation (NLG)
- Chatbots and Conversational AI
- Question Answering Systems
- Summarization
- Transfer Learning
- Reinforcement Learning
- Neural Networks and Deep Learning
- Generative Adversarial Networks (GANs)
- Vector Databases
- Langchain
- LLMs
- Retrieval Augmented Generation
- Ensemble Methods
- Optimization Techniques
- Anomaly Detection
- Graph-based Models
- Bayesian Learning
- Active Learning and Semi-Supervised Learning
- ML Audio
- Diffusion Models
- Meta-Learning
- Few-Shot and Zero-Shot Learning
- Self-Supervised Learning
- Training and Fine-tuning LLMs
- https://www.coursera.org/articles/data-science-vs-machine-learning?trk_ref=articles_unified_description_page_recs_card
- Preprocessing and Feature Engineering
- Clustering and Dimensionality Reduction
- Time Series Analysis
- Recommendation Systems
- Tools and Frameworks
- Case Studies and Applications
- Robotics Control Systems
- SLAM (Simultaneous Localization and Mapping)
- Human-Robot Interactions
- Image Segmentation
- Pose Estimation
- 3D Computer Vision
- Quantum Machine Learning
- Neurosymbolic AI
- Edge AI
- Medical Image Analysis
- Algorithmic Trading
- AI for Social Good
- Adversarial Attacks and Defenses
- Model Robustness
- Bias and Fairness in AI
- MLOps
- AI Hardware Accelerators