Generative AI From Beginner to Paid Professional, Part 2
Master Prompt Design, Gemini Multimodal in Vertex AI Studio, LangChain, Launching & Deploying Generative AI ProjectsBy Bolakale Aremu
Length5h 30m
About this audiobook
Unlock the Future of Generative AI and Skyrocket Your Career.
This book is your comprehensive roadmap from grasping the fundamentals of AI to mastering the tools and techniques that will set you apart in today’s AI-driven world. Perfect for anyone serious about a career in AI, this book bridges the gap between knowledge and action, giving you the tools to earn, build, and innovate with generative AI technologies. Whether you’re building your first AI project or refining your professional skills, this is the guide you’ve been waiting for.
Packed with hands-on projects and practical exercises, this book empowers you to build and launch your very own generative AI solutions. By the end, you’ll not only be equipped with in-demand AI skills but also be prepared to launch your AI projects in the real world.
Welcome to Generative AI from Beginner to Paid Professional, Part 2: Master Prompt Design, Gemini Multimodal in Vertex AI Studio, LangChain, Launching & Deploying Generative AI Projects.
In Part 2 of this transformative guide, you'll delve deep into powerful AI frameworks and cutting-edge technologies, including Gemini Multimodal, Vertex AI Studio, and LangChain, gaining the expertise needed to design custom AI solutions and deploy scalable AI projects. Whether you’re an aspiring professional or a seasoned developer, this book is your step-by-step companion to navigating the evolving landscape of generative AI.
What You’ll Learn:
Master Prompt Design: Craft perfect prompts that make your AI work for you, no matter the use case.
Gemini Multimodal & Vertex AI Studio: Learn to integrate multimodal models into your AI pipeline, revolutionizing how you build intelligent systems that understand and generate both text and images.
LangChain for Real-World AI Projects: Leverage LangChain to create robust, API-powered workflows that bring your AI projects to life.
Launching & Deploying AI Projects: From conceptualization to deployment, turn your AI ideas into real-world applications with proven strategies.
Audiobook details
GenreTechnology, Science and Nature
Length5 hrs 30 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
Publish dateNov 10, 2024
LanguageEnglish
Table of contents
10. About The Series
21. Introduction: 1.1. Learning Objectives
32. Generative AI Workflow
42.1. Introduction to the Vertex AI Studio
52.2. Generative AI
Mostrar todos os capítulosVer menos
62.3. Foundation Models: 2.3.1. How Foundation Models Power Your Applications
73. The Gemini Multimodal
83.1. Gemini Business Use Cases
93.2. How to Interact with Gemini Multimodal
103.2.1. Starting with a Prompt
113.2.2. Anatomy of a Prompt
123.3. Language Capabilities: Prompt Design Modes
133.3.1. Free-form Prompt Design: Hands-on Practice 1
143.3.2. Structured Prompt Design: Hands-on Practice 2
153.3.3. Practical Examples for Understanding Model Parameters (Temperature, Top K & Top P)
163.4. Model Tuning
173.4.1. How to Customize & Tune a GenAI Model
183.4.2. How to Start a Tuning Job in the Vertex AI Studio
193.5. Wrap up
204. LangChain for Generative AI
214.1. Introduction to LangChain and Generative AI: 4.1.2. Setting Up Your Environment for LangChain Development
224.2. Core Concepts and Architecture
234.2.1. Understanding Chains in LangChain
244.2.2. Modules: The Building Blocks of LangChain
254.2.3. Key Components: Models, Prompts, and Parsers
264.3. Models and Prompt Design
274.3.1. Choosing the Right Model for Your Needs
284.3.2. Prompt Design Fundamentals
294.3.3. Advanced Prompting Techniques for Custom Responses
304.4. Building Chains and Workflows
314.4.1. Creating Simple Chains
324.4.2. Designing Multi-Step Workflows
334.4.3. Handling Errors and Fallbacks in Chains
344.5. Integrating APIs with LangChain
354.5.1. API Integration Basics
364.5.2. Leveraging External APIs for Enhanced Functionality
374.5.3. Using Web Scraping and Other Data Sources
384.6. Memory Management in LangChain
394.6.1. Introduction to Memory in AI Models
404.6.2. Implementing State Management in LangChain
414.6.3. Best Practices for Memory Optimization
424.7. User Input and Parsing Responses
434.7.1. Capturing and Validating User Input
444.7.2. Parsing Model Outputs
454.7.3. Using Parsers for Structured Data Handling
464.8. Custom Modules and Extensions
474.8.1. Writing Custom Modules in LangChain
484.8.2. Extending LangChain’s Capabilities with Plugins
494.8.3. Using OpenAI Plugins and Integrations
504.9. Debugging and Optimization
Series
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