Tech Horizon with Anand Vemula

  Generative AI in Education: Shaping the Learning Horizon  Link to Book -  Amazon.com: Generative AI in Education: Shaping the Learning Horizon eBook : Vemula, Anand: Kindle Store Generative AI is transforming education by personalizing learning experiences, enhancing creativity, and improving access to knowledge. Through AI-driven tools, students can now receive tailored instruction, catering to individual learning styles and needs. This approach promotes deeper engagement and allows learners to progress at their own pace, improving overall retention and comprehension. Teachers, too, benefit from generative AI. It assists with automating administrative tasks like grading, freeing up more time for meaningful student interactions. Additionally, AI-powered content creation tools can help educators develop customized learning materials, ranging from interactive lessons to virtual simulations. Beyond personalization, generative AI fosters creativity by enabling students to experiment with

  Retrieval-Augmented Generation (RAG) using Large Language Models Link to Book - https://www.amazon.com/dp/B0CXZG92HZ Retrieval-Augmented Generation (RAG) is a powerful technique that combines the strengths of retrieval-based systems and generative AI, particularly large language models (LLMs). While LLMs like GPT excel at generating human-like text, they can struggle with fact-based or real-time information. RAG bridges this gap by allowing the model to retrieve relevant external data during the generation process. RAG works by first retrieving documents or data from an external knowledge base, such as databases, websites, or enterprise systems, based on the user’s query. The retrieved information is then fed into the generative model, allowing it to create more accurate, context-aware responses. This approach is useful in applications such as customer support, where up-to-date, factual information is critical, or in research settings where LLMs can generate insights while pulling in

  Tutorials - Building Generative AI-Based Applications on AWS Bedrock - Step by step with code Link to Book -  Amazon.com: Tutorials - Building Generative AI-Based Applications on AWS Bedrock - Step by step with code eBook : Vemula, Anand: Kindle Store AWS Bedrock provides a powerful platform to build and scale generative AI-based applications with ease. Whether you're developing AI-powered chatbots, image generation tools, or personalized content systems, AWS Bedrock offers ready-to-use foundation models and a streamlined environment for integrating generative AI into applications. This guide walks through the steps to create a generative AI app using AWS Bedrock, without diving into code. Step 1: Set Up AWS Environment Begin by logging into your AWS account and accessing AWS Bedrock from the console. Ensure that your account has appropriate IAM permissions to interact with Bedrock, including managing models and deploying applications. Step 2: Choose a Foundation Model AWS Bedroc

  Generative AI on Google Cloud for Application Development: Real-World Applications, Use Cases, Code Examples, and Scenarios Link to Book -  Amazon.com: Generative AI on Google Cloud for Application Development: Real-World Applications, Use Cases, Code Examples, and Scenarios eBook : Vemula, Anand : Kindle Store Generative AI is transforming industries by automating content creation, personalizing user experiences, and driving innovation in application development. Google Cloud’s robust suite of AI tools, such as Vertex AI and BigQuery, empowers developers to seamlessly integrate generative AI into their applications. This blog explores real-world applications, use cases, and practical scenarios where generative AI on Google Cloud enhances productivity and innovation. Real-World Applications Content Creation : Google Cloud’s generative AI tools streamline the process of creating high-quality text, visuals, and code. For instance, marketing teams use AI models to automatically generate

  Python for Generative AI: Practical Techniques, Applications, and Code Examples Link to Book -  Amazon.com: Python for Generative AI: Practical Techniques, Applications, and Code Examples eBook : Vemula, Anand: Kindle Store Python has become a leading language for generative AI due to its ease of use, robust libraries, and widespread community support. It empowers developers to build models that create everything from text and images to music and code. Its flexibility allows users to handle complex AI tasks with minimal complexity. Popular Python libraries such as TensorFlow and PyTorch simplify the process of building and training generative models, while tools like Hugging Face Transformers provide pre-trained models that can be fine-tuned for specific use cases, such as generating text or summarizing data. Additionally, frameworks like Keras allow for rapid prototyping of deep learning models. Applications of Python in generative AI are vast. Businesses use it to automate cont

  Mastering Generative AI and Data Engineering: Techniques, Tools, and Practical Applications: A Comprehensive Guide to Building, Scaling, and Securing Generative AI Solutions Link to Book -  Amazon.com: Mastering Generative AI and Data Engineering: Techniques, Tools, and Practical Applications: A Comprehensive Guide to Building, Scaling, and Securing Generative AI Solutions eBook : Vemula, Anand: Kindle Store In today’s rapidly evolving digital landscape, the synergy between Generative AI and data engineering has become crucial for building advanced, scalable, and secure AI solutions. With AI systems producing everything from text to images and even code, leveraging robust data pipelines and engineering practices is key to unlocking the full potential of these systems. This blog post delves into the techniques, tools, and applications that professionals can use to develop and scale generative AI solutions while ensuring data security. The Role of Data Engineering in Generative AI Gene

  Master Generative AI with LLMs: A Practical Guide with Exercises Link to Book -  Amazon.com: Master Generative AI with LLMs: A Practical Guide with Exercises eBook : Vemula, Anand: Kindle Store Generative AI has transformed how we interact with technology, and large language models (LLMs) like GPT-4 are at the forefront of this revolution. Mastering generative AI with LLMs involves understanding their capabilities, training methodologies, and practical applications. This guide will walk you through the essentials and provide practical exercises to help you gain hands-on experience. 1. Understanding LLMs Large language models (LLMs) are designed to generate human-like text based on the input they receive. They leverage vast amounts of data and advanced neural network architectures to understand context and generate coherent responses. Key concepts to grasp include: Tokenization : The process of converting text into smaller units (tokens) that the model can process. Attention Mechanism