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Free Download Multimodal RAG - AI Search & Recommender Systems with GPT-4 Published 9/2024 Created by Paulo Dichone | Software Engineer, AWS Cloud Practitioner & Instructor MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 22 Lectures ( 1h 31m ) | Size: 1.2 GB Mastering Multimodal RAG: Build AI-Powered Search & Recommender Systems with GPT-4, CLIP, and ChromaDB What you'll learn: Understand and implement Retrieval-Augmented Generation (RAG) with multimodal data (text, images). Build AI-powered search and recommender systems using GPT-4, CLIP, and ChromaDB. Generate and utilize text and image embeddings to perform multimodal searches. Develop interactive applications with Streamlit to handle user queries and provide AI-driven recommendations Requirements: Basic understanding of Python programming. Familiarity with machine learning concepts (embeddings, vectors). No prior experience with multimodal systems is needed, but knowledge of AI tools like GPT or CLIP will be helpful. A computer with internet access and the ability to install Python libraries like Streamlit, OpenAI, and ChromaDB. Description: Are you ready to dive into the cutting-edge world of AI-powered search and recommender systems? This course will guide you through the process of building Multimodal Retrieval-Augmented Generation (RAG) systems that combine text and image data for advanced information retrieval and recommendations.In this hands-on course, you'll learn how to leverage state-of-the-art tools such as GPT-4, CLIP, and ChromaDB to build AI systems capable of processing multimodal data-enhancing traditional search methods with the power of machine learning and embeddings.What You'll Learn:Master Multimodal RAG: Understand the concept of Retrieval-Augmented Generation (RAG) and how to implement it for both text and image-based data.Build AI-Powered Search & Recommendation Systems: Learn how to construct search engines and recommender systems that can handle multimodal queries, using powerful AI models like GPT-4 and CLIP.Utilize Embeddings for Cross-Modal Search: Gain practical experience generating and using embeddings to enable search and recommendations based on text or image input.Develop Interactive Applications with Streamlit: Create user-friendly applications that allow real-time querying and recommendations based on user-provided text or image data.Key Technologies You'll Work With:GPT-4: A cutting-edge language model that powers the AI-driven recommendations.CLIP: An advanced AI model for generating image and text embeddings, making it possible to search images with text.ChromaDB: A high-performance vector database that enables fast and efficient querying for multimodal embeddings.Streamlit: A simple yet powerful framework for building interactive web applications.No prior experience with multimodal systems? No problem!This course is designed to make advanced AI concepts accessible, with detailed, step-by-step instructions that guide you through each process-from generating embeddings to building complete AI systems. Basic Python knowledge and a curiosity for AI are all you need to get started.Enroll today and take your AI development skills to the next level by mastering the art of multimodal RAG systems! Who this course is for: Aspiring AI Developers: Individuals looking to build AI-powered applications that integrate text and image data. Data Scientists: Professionals aiming to enhance their skills in multimodal data processing and retrieval. Machine Learning Engineers: Those seeking to implement advanced search and recommender systems using state-of-the-art models. Homepage https://anonymz.com/https://www.udemy.com/course/multimodal-rag/ Rapidgator https://rg.to/file/089f61804f8d40592dbf48b60b41a26c/vglnz.Multimodal.RAG.AI.Search..Recommender.Systems.with.GPT4.part1.rar.html https://rg.to/file/6370fb9e607e28fdc88206885992158e/vglnz.Multimodal.RAG.AI.Search..Recommender.Systems.with.GPT4.part2.rar.html Fikper Free Download https://fikper.com/XnSbtleiyl/vglnz.Multimodal.RAG.AI.Search..Recommender.Systems.with.GPT4.part1.rar.html https://fikper.com/4WLjjrOdGv/vglnz.Multimodal.RAG.AI.Search..Recommender.Systems.with.GPT4.part2.rar.html No Password - Links are Interchangeable

Free Download DSPy - Develop a RAG app using DSPy, Weaviate, and FastAPI Published 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 1h 51m | Size: 1.12 GB Master Full-Stack RAG App Development with FastAPI, Weaviate, DSPy, and React What you'll learn Build and Deploy a Full-Stack RAG Application Efficient Data Management with Weaviate Document Parsing and File Handling Implement Advanced Backend Features with FastAPI Requirements Basic Knowledge of Python Familiarity with REST APIs Understanding of Frontend Development Development Environment Setup Description Learn to build a comprehensive full-stack Retrieval Augmented Generation (RAG) application from scratch using cutting-edge technologies like FastAPI, Weaviate, DSPy, and React. In this hands-on course, you will master the process of developing a robust backend with FastAPI, handling document uploads and parsing with DSPy, and managing vector data storage using Weaviate. You'll also create a responsive React frontend to provide users with an interactive interface. By the end of the course, you'll have the practical skills to develop and deploy AI-powered applications that leverage retrieval-augmented generation techniques for smarter data handling and response generation.Here's the structured outline of your course with sections and lectures:Section 1: IntroductionLecture 1: IntroductionLecture 2: Extra: Learn to Build an Audio AI AssistantLecture 3: Building the API with FastAPISection 2: File UploadLecture 4: Basic File Upload RouteLecture 5: Improved Upload RouteSection 3: Parsing DocumentsLecture 6: Parsing Text DocumentsLecture 7: Parsing PDF Documents with OCRSection 4: Vector Database, Background Tasks, and FrontendLecture 8: Setting Up a Weaviate Vector StoreLecture 9: Adding Background TasksLecture 10: The Frontend, Finally!Section 5: Extra - Build an Audio AI AssistantLecture 11: What You Will BuildLecture 12: The FrontendLecture 13: The BackendLecture 14: The End Who this course is for Backend Developers wanting to learn how to build APIs with FastAPI and integrate AI-driven features like document parsing and vector search. Full-Stack Developers seeking to gain practical experience in combining a React frontend with an AI-powered backend. Data Scientists and AI Practitioners who want to explore new ways to implement retrieval-augmented generation models for real-world applications. AI Enthusiasts curious about vector databases like Weaviate and the emerging field of RAG, with the motivation to learn and build AI-based apps from scratch. Homepage https://www.udemy.com/course/dspy-develop-a-rag-app-using-dspy-weaviate-and-fastapi/ Rapidgator https://rg.to/file/4f9e11b273a58d7c1f8970e4cb9c3661/ohdou.DSPy.Develop.a.RAG.app.using.DSPy.Weaviate.and.FastAPI.part1.rar.html https://rg.to/file/4e6240b776b2e7d50c5eafacd4ff1c1b/ohdou.DSPy.Develop.a.RAG.app.using.DSPy.Weaviate.and.FastAPI.part2.rar.html Fikper Free Download https://fikper.com/Ae2h0a6SuW/ohdou.DSPy.Develop.a.RAG.app.using.DSPy.Weaviate.and.FastAPI.part1.rar.html https://fikper.com/HDq2JkFu32/ohdou.DSPy.Develop.a.RAG.app.using.DSPy.Weaviate.and.FastAPI.part2.rar.html No Password - Links are Interchangeable

Free Download RAG Tuned AIs with the Cohere API Platform Released 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill Level: Beginner | Genre: eLearning | Language: English + srt | Duration: 21m | Size: 56 MB Explore the Cohere API platform and its powerful tools for fine-tuning AI models with Retrieval-Augmented Generation (RAG) techniques. Starting with an overview of Cohere's unique cloud-agnostic approach with their own fast release models focused on business needs. Learn to build and optimize AI models that are deployable across many cloud environments. Explore Cohere Developer tools and experiment with model customization and discover tools for building AI-driven applications. Homepage https://www.linkedin.com/learning/rag-tuned-ais-with-the-cohere-api-platform TakeFile https://takefile.link/f6q71th45y9u/gelgb.RAG.Tuned.AIs.with.the.Cohere.API.Platform.rar.html Rapidgator https://rg.to/file/3eec92efbe5e4eb7bd585b5d53bcabc6/gelgb.RAG.Tuned.AIs.with.the.Cohere.API.Platform.rar.html Fikper Free Download https://fikper.com/Nwh5xdmgul/gelgb.RAG.Tuned.AIs.with.the.Cohere.API.Platform.rar.html No Password - Links are Interchangeable

Free Download RAG and Generative AI with Python 2024 Published 9/2024 Created by Diogo Alves de Resende MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 86 Lectures ( 9h 5m ) | Size: 6.33 GB Mastering Retrieval-Augmented Generation (RAG), Generative AI (Gen AI), Prompt Engineering and OpenAI API with Python What you'll learn: Gain a solid foundation in information retrieval concepts, including tokenization, preprocessing, indexing, querying, and ranking. Implement various retrieval models in Python, such as the Vector Space Model, Boolean Retrieval, and Probabilistic Retrieval, using real-world datasets. Understand how text generation models work, including the principles behind transformers and attention mechanisms. Acquire hands-on experience in using Python libraries to build, fine-tune, and deploy generative models like GPT for various text generation tasks. Learn how to effectively combine retrieval and generative models to build robust Retrieval-Augmented Generation (RAG) systems. Utilize Python for advanced RAG system components, such as tokenization, embedding creation, FAISS indexing, and context distance definition. Explore the integration of OpenAI's API in RAG systems to enhance retrieval and generation capabilities, including prompt engineering and embedding strategies. Develop skills to process and integrate unstructured data formats (Excel, Word, PowerPoint, EPUB, PDF) into RAG systems using Python. Learn to build multimodal RAG systems that combine text, audio, and image data using Python, leveraging models like CLIP and Whisper. Master techniques to improve the accuracy, efficiency, and effectiveness of RAG systems, preparing you for real-world applications and advanced AI research. Requirements: Python Proficiency (For loops, Functions) Description: Are you struggling to build RAGs? As the amount of digital content grows exponentially, it becomes increasingly challenging to create AI models that can efficiently sift through vast data to provide accurate and meaningful responses. Traditional search engines and basic AI models often fall short in delivering the context-aware results needed in today's fast-paced digital landscape.RAG and Generative AI with Python is designed to solve this problem by teaching you how to build powerful Retrieval-Augmented Generation (RAG) systems using Python. This course will guide you through the essentials of combining retrieval techniques with generative models to develop applications that are both highly responsive and contextually accurate.Throughout this course, you will:Understand RAG Systems: Learn how to integrate retrieval and generation to enhance your AI models' capabilities, making them more effective at understanding and generating relevant content.Learn Practical Python Applications: Gain hands-on experience with Python libraries and frameworks, enabling you to implement RAG systems and generative models from scratch.Explore Generative AI and Prompt Engineering: Delve into the mechanics of generative models and the art of prompt engineering to refine AI outputs, ensuring they meet specific user needs.Utilize OpenAI's API for Advanced Applications: Discover how to leverage OpenAI's API to enhance your models, adding a new layer of sophistication to your AI solutions.Handle Various Data Formats in AI Systems: Develop skills to manage unstructured data types, including text, images, and audio, and integrate them into multimodal RAG systems for comprehensive AI applications.Optimize AI Models for Real-World Use: Learn strategies to fine-tune your AI models for improved efficiency, accuracy, and performance in practical scenarios.This course is perfect for data scientists, software developers, AI enthusiasts, and anyone with a basic knowledge of Python who wants to build smarter, more efficient AI systems. If you're ready to overcome the limitations of traditional models and lead the charge in AI innovation, this course is for you.Take the next step in your AI journey with RAG and Generative AI with Python and learn how to create the advanced AI tools that the world needs now. Enroll today and start transforming the way you build AI systems! Who this course is for: Data Scientists and Machine Learning Engineers looking to deepen their knowledge of generative AI systems. AI Researchers and Enthusiasts interested in exploring the latest advancements in (RAG) and generative AI technologies. oftware Developers and Programmers who want to expand their skill set to include AI and machine learning techniques. Technical Product Managers and AI Strategists who manage AI projects and need a deeper technical understanding of how RAG systems work and their potential applications. AI Consultants and Data Analysts aiming to add AI capabilities to their skillset Entrepreneurs and business leaders in the tech space who want to understand the potential of RAG systems and generative AI to innovate. 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Zadymiarze / Rag Union / Tryapichnyy soyuz (2015) PL.WEB-DL.Xvid-K12 / Lektor PL Re??yseria: Mikhail Mestetskiy Scenariusz: Mikhail Mestetskiy Gatunek: Dramat, Komedia Kraj: Rosja Rok produkcji: 2015 Czas trwania: 98 min. Szmaciany Sojusz to grupa trzech przyjaci???? z r????nym ??yciowym baga??em. Pe??ni energii, nieustraszeni, niezwyciÄ???eni, ze zwinno??ciÄ? wiewi??rek skaczÄ? przez nagrobki i po dachach samochod??w. WierzÄ?, ??e si??Ä? woli sÄ? w stanie przesunÄ?Ä? mury. Parkour i sztuka to ich spos??b na zastany porzÄ?dek rzeczy. Taka w??a??nie jest Szmaciana Rewolucja, kt??ra zmieni ??wiat. Gdyby Wania nie by?? taki nie??mia??y, to pewnie nie wziÄ???by tej pracy, a tak trafi?? na cmentarz jako ??ywa reklama zak??adu pogrzebowego. Z drugiej strony, gdyby nie by?? wtedy na cmentarzu, nie pozna??by Szmaciarzy. Szmaciarze nie wylÄ?dowaliby w domku po jego babci. Domek by wciÄ??? sta??... Ale Wania by?? nie??mia??y i wziÄ??? tÄ? pracÄ?. https://openload.co/f/wAT8SJdJsfg/Zadymiarze_%282015%29_PL.WEB-DL.Xvid-K12.avi

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