Dive into the world of advanced AI with "Python LangChain for RAG Beginners" ✔ Learn how to code Agentic RAG Powered Chatbot Systems. ✔ Empower your Agents with Tools ✔ Learn how to Create your Own Agents This comprehensive guide takes you on a journey through LangChain, an innovative framework designed to harness the power of Generative Pre-trained Transformers (GPTs) and other large language models (LLMs) for creating sophisticated AI-driven applications. Starting from the basics, this book provides a detailed understanding of how to effectively use LangChain to build, customize, and deploy AI applications that can think, learn, and interact seamlessly. You will explore the core concepts of LangChain, including prompt engineering, memory management, and Retrieval Augmented Generation (RAG). Each chapter is packed with practical examples and code snippets that demonstrate real-world applications and use cases. Key highlights include: Getting Started with LangChain: Learn the foundational principles and set up your environment. Advanced Prompt Engineering: Craft effective prompts to enhance AI interactions. Memory Management: Implement various memory types to maintain context and continuity in conversations. Retrieval Augmented Generation (RAG): Integrate external knowledge bases to expand your AI's capabilities. Building Intelligent Agents: Create agents that can autonomously perform tasks and make decisions. Practical Use Cases: Explore building a chat agent with web UI that allows you chatting with documents, web retrieval, vector databases for long term memory and much more ! Whether you are an AI enthusiast, a developer looking to integrate AI into your projects, or a professional aiming to stay ahead in the AI-driven world, " Python LangChain for RAG Beginners" provides the tools and knowledge to elevate your AI skills. Embrace the future of AI and transform your ideas into powerful, intelligent applications with LangChain.

Machine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data, lowers costs, and helps you ship models faster. Human-in-the-loop machine learning lays out methods for humans and machines to work together effectively. You'll find best practices on selecting sample data for human feedback, quality control for human annotations, and designing annotation interfaces. You'll learn to dreate training data for labeling, object detection, and semantic segmentation, sequence labeling, and more. The book starts with the basics and progresses to advanced techniques like transfer learning and self-supervision within annotation workflows.

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

We are entering the next wave of digital transformation. Artificial intelligence has an ever-increasing significance in our daily lives, and there is no difference when it comes to our workplaces. It is up to you to choose how to utilize these new tools to sharpen your organization’s competitive advantage, improve your team’s well-being, and help your business thrive. In The AI-Powered Workplace, author Ronald Ashri provides a map of the digital landscape to guide you on this timely journey. You’ll understand how the combination of AI, data, and conversational collaboration platforms—such as Slack, Microsoft Teams, and Facebook Workplace—is leading us to a radical shift in how we communicate and solve problems in the modern workplace. Our ability to automate decision-making processes through the application of AI techniques and through modern collaboration tools is a game-changer. Ashri skillfully presents his industry expertise and captivating insights so you have a thorough understanding of how to best combine these technologies with execution strategies that are optimized to your specific needs. The AI-Powered Workplace is an essential technical, cultural, and business handbook that arms you with clear steps to redefine and improve how you get work done. Software is now a proactive workplace partner revolutionizing all aspects of our professional lives from how we collaborate in the digital sphere to the literal physical environments in which we operate our business. This book not only ensures that you do not get left behind, but that you are consistently light years ahead of the pack. What You'll Learn Learn how the introduction of AI-powered applications in the workplace replaces or augments our capabilities and enables activities that were not possible beforeRealize how the combination of AI, data, and messaging platforms (Slack, Microsoft Teams, Skype, WhatsApp) leads to a radical shift in how we communicate, collaborate, and solve problemsDevelop strategies for the digital transformation of organizations through the use of AI-powered applications (from simple chatbots to more complex conversational applications) that operate within messaging environments we use to collaborate with our colleagues dailyKnow the dangers and ethical questions that the introduction of these technologies can cause in the workplace Who This Book is For Professionals at all levels interested in learning how AI, conversational platforms, and data can change organizations, including but not limited to team leaders, managers, and CxOs

This book is written above all for artificial intelligence directors and engineers with some experience in the field. For instance, you may be an AI executive who has no technical background or an AI engineer who has implemented several projects in the past. This book was written with you in mind. The practical tips and insight will make you more productive with regard to idea development, and more prosperous in terms of communication with other professionals. Sharing my experience in building machine learning products, I have aimed to describe sophisticated concepts in simple terms. I hope you enjoy reading this book. This book helps you: identify use-cases where AI creates value, learn fundamentals in simple words, prevent mistakes to build an AI solution, learn best practices and increase efficiency, gain insight to build innovative solutions, manage development risks and save money.

Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.