AI Concepts Developers Should Learn
Machine Learning
: Core algorithms, statistics, and model training techniques.Deep Learning
: Neural networks with multiple layers that learn complex patterns automatically.Neural Networks
: Layered structures that model complex, nonlinear relationships in data.NLP
: Techniques for processing and understanding human language in text form.Computer Vision
: Algorithms that interpret and analyze images and visual data.Reinforcement Learning
: Training agents through rewards and penalties in interactive environments.Generative Models
: AI systems that create new data based on learned patterns.LLM
: Large language models that generate human-like text from training data.Transformers
: Self-attention architecture that powers most modern AI language models.Feature Engineering
: Selecting and designing input features to improve model performance.Unsupervised Learning
: Finding patterns in data without using labeled examples.Bayesian Learning
: Using probabilistic methods to incorporate uncertainty into models.Prompt Engineering
: Crafting effective inputs to guide AI model outputs optimally.AI Agents
: Autonomous systems that observe environments and take intelligent actions.Fine-Tuning Models
: Adapting pre-trained models for specific tasks and domains.Multimodal Models
: AI systems that process text, images, audio, and video together.Embeddings
: Converting data into numerical vectors that machines can process effectively.Vector Search
: Finding similar items by comparing their mathematical vector representations.Model Evaluation
: Testing and measuring how well AI models perform on tasks.AI Infrastructure
: Building scalable systems to deploy and run AI applications.