Generative-Ai

Comprehensive overview of generative AI tools for code generation, including capabilities, strengths, limitations, and key platforms such as ChatGPT Gemini, Copilot, Polycoder, Watson Code Assistant, Amazon CodeWhisperer, Tab9 and Repl.it. Covers productivity, best practices, and ethical considerations.

Overview of generative AI tools for audio and video, including speech generation, music creation, audio enhancement, and video synthesis. Covers key platforms, capabilities, and real-world applications in creative and professional domains.

This document covers the wide-ranging applications of Generative AI across various domains such as content creation, drug discovery, engineering finance and healthcare.

This document provides an overview of leading tools and technologies for image generation using generative AI, including DALL-E, Stable Diffusion, StyleGAN Craiyon, Freepik, Picsart, Fotor, Deep Art Effects, DeepArt.io, Midjourney Microsoft Bing Image Creator, and Adobe Firefly.

This document introduces retrieval-augmented generation (RAG), its components benefits, limitations of generative AI, and practical applications, with a focus on implementation using Google Cloud tools.

This document provides an overview of large language models (LLMs), their foundation model origins, generative capabilities, and business applications. It explains how LLMs are trained, their advantages, and the role of prompting and tuning in real-world use cases.

Summarizes key concepts in machine learning, deep learning, generative AI cognitive computing, NLP, computer vision, IoT, cloud, and edge computing with real-world applications and model architectures.

This document outlines essential tools and real-world applications of generative AI, including language, image, audio, and video generation, and highlights industry adoption by leading companies.

This document compares traditional AI and generative AI, highlighting their architectures, data sources, feedback mechanisms, and business applications. It explains how generative AI leverages large language models and massive datasets to enable new capabilities.

Comprehensive summary and cheatsheet covering generative AI integration in software development, including DevOps automation, security enhancement threat detection, platforms, secure coding tools, AI-powered debugging documentation and career opportunities.

Exploration of integrating AI-powered features into mobile applications for enhanced user experiences and innovative functionalities, particularly in photo memory applications.

Comprehensive guide to AI considerations in software development, covering ethics, fairness, explainability, robustness, transparency, privacy intellectual property, security, compliance, and bias mitigation for responsible AI implementation.

Comprehensive guide to integrating generative AI in software development workflows, covering AI-powered code review, debugging, documentation generation, education, training tools, and practical exercises for automation and enhanced productivity.

Final assignment project to create CodeCraftHub personalized learning platform using generative AI, ChatGPT, Node.js, MongoDB, Express.js, with requirements gathering, database design, code generation, testing, and Docker deployment.

This document provides a comprehensive introduction to generative AI, tracing its evolution from traditional AI to foundation models, and exploring its impact on software development and its relationship with machine learning deep learning, and LLMs.