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 traces the evolution of Generative AI, from early rule-based systems in the 1950s to the latest advancements in Large Language Models (LLMs), highlighting key milestones like GANs and Transformers.

This document explores the diverse capabilities of Generative AI, including text, image, audio, video, code, data generation, and virtual world creation with real-world applications and examples. It also covers the latest advancements in multimodal AI, AI agents, and the impact of generative AI on various industries.

This document explains hallucination in large language models, its types causes, and practical strategies to minimize fabricated or inaccurate outputs in AI-generated content.

This document summarizes key concepts from the module, including AI agents robotics, cobots, RPA, generative AI, business adoption, AI tools, and career opportunities, providing a comprehensive overview of modern AI applications.

This document explores the strengths and limitations of human and AI decision-making, using fraud detection as a case study, and examines how augmented intelligence can combine the best of both.

This document explores the evolving landscape of AI careers, highlighting technical and non-technical roles, required skills, and strategies for transitioning into the AI field across diverse industries.

This document compares AI adoption frameworks used by Amazon, OpenAI, and Facebook, detailing their phases and tools for effective, ethical, and scalable AI integration in business operations.

This document explains the IBM AI Ladder framework for AI adoption, details each stage from data collection to business integration, and explores the shift from +AI to AI+ for holistic transformation.

This document explores the challenges of large language models and how retrieval-augmented generation (RAG) addresses issues of outdated knowledge and lack of sources, with practical examples.

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 module explores the evolution from traditional AI to generative AI and foundation models, explaining modes of AI consumption, the importance of open knowledge, and the impact of value creation and extraction in the AI economy.

This module explores how AI is transforming business operations, automating workflows, enhancing decision-making, and driving efficiency and innovation across industries such as customer service, HR, accounting, marketing manufacturing, and healthcare.

This module explores the shift from monolithic models to compound AI systems highlighting how integrating models with tools and databases enables more flexible, accurate, and adaptable solutions for real-world tasks.

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.

This document introduces generative AI models, their types, and applications. It explains how these models use machine learning and deep learning to create new content, and highlights the differences between unimodal and multimodal models.

This document explains the key differences between machine learning and deep learning, using practical analogies and examples. It covers their relationship, data requirements, feature engineering, and the role of neural networks in deep learning.

This document introduces neural networks, their structure, types, and training process. It explains how neural networks are inspired by the human brain and highlights their applications in pattern recognition, image analysis, and sequential data processing.

This document provides an overview of deep learning, its key concepts applications in various fields, and the different types of models used. It also covers the training process and recent advancements in the field.