• 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