GenAI

AI is the simulation of human intelligence by machines. Machine Learning is a subset of AI that enables systems to learn from data and improve over time without being explicitly programmed.

Neural networks are computational models inspired by the human brain. Deep learning uses multi-layered neural networks to model complex patterns in data, powering advances in vision, language, and more.

LLMs are deep learning models trained on vast text data to understand and generate human-like language. Examples include GPT, BERT, and Llama.

GANs consist of two neural networks (generator and discriminator) competing to create realistic data. They are widely used for image, video, and audio generation.

Training is the process of teaching a model using data. Fine-tuning adapts a pre-trained model to a specific task or dataset, improving performance for specialized applications.

Prompt engineering involves crafting effective inputs (prompts) to guide generative models like LLMs to produce desired outputs.

Text generation uses models to create human-like text, such as articles, summaries, or code, based on input prompts or context.

Image generation models (like GANs or diffusion models) create new images from text descriptions or random noise, enabling creative and practical applications.

Code generation uses AI models to write code snippets, functions, or even entire programs from natural language instructions or examples.

Managing datasets and models involves versioning, tracking experiments, and ensuring reproducibility for reliable AI development and deployment.

GenAI tools can generate articles, marketing copy, stories, and more, accelerating content creation for businesses and individuals.

AI assists in software development by generating code, automating tests, and providing intelligent suggestions, boosting productivity and quality.

Generative AI creates original artwork, designs, and music, empowering artists and designers with new creative tools and possibilities.