Materials for 21.06.2026 » Page 294 » Dl4All - Download free For All: software and educational resource.

Building Learning Communities with Character: How to Integrate Academic, Social, and Emotional Learning By Bernard Novick, Jeffrey S. Kress, Maurice J. Elias
2002 | 143 Pages | ISBN: 087120665X | PDF | 2 MB
Today's students need to learn more than just reading, writing, and arithmetic. They need to learn life skills to successfully manage tasks, form relationships, solve everyday problems, and adapt to the demands of simply growing up. To satisfy those needs, many educators seek effective and lasting programs for their students' academic, emotional, and social growth. This book presents a nine-step, problem-solving approach to help educators not only create such a program, but also shape the school climate to sustain and nurture it. Combining three decades of work with individual schools and districts, authors Bernard Novick, Jeffrey S. Kress, and Maurice J. Elias provide experienced insight to overcoming obstacles to social-emotional learning and character education programs. Their organized approach deals with the implementation process stage by stage, including: * Assessing your school's readiness for change * Setting goals for your program * Anticipating details and roadblocks * Obtaining feedback to modify your implemented program * Creating high standards for accomplishment in academics and character Any educator who is confused by the avalanche of different terminologies and methodologies surrounding social-emotional learning will benefit from this book. This practical and concise guide will help educators create an effective and goal-oriented environment in every school, one where students, staff, and parents are continually sustained in a caring community of learners.

Building Generative AI on AWS Bedrock: Develop LLM Applications with Python, RAG, and Agents
English | 14 Dec. 2025 | ASIN: B0G6X5S7B4 | 414 pages | Epub | 296.06 KB
Stop building "toy" chatbots. Start architecting enterprise-grade Generative AI on AWS. The era of experimental AI is over. Businesses today demand secure, scalable, and intelligent applications that solve real problems. Building Generative AI on AWS Bedrock is your definitive, hands-on guide to mastering the managed service that is redefining how developers build with Large Language Models (LLMs). Written for Python developers and cloud architects, this book bypasses the fluff and dives straight into the code. You won't just learn "what" Generative AI is; you will learn exactly how to implement it using the AWS SDK (Boto3) and the Amazon Bedrock API. From your first API call to deploying autonomous Agents that can execute complex business tasks, this book takes you through the entire engineering lifecycle. What You Will Build and Master: The Bedrock Foundation: Navigate the control plane vs. data plane, manage quotas, and programmatically switch between top-tier models like Anthropic Claude 3 , Meta Llama 3 , and Amazon Titan without rewriting your code. Production-Grade RAG: Move beyond simple tutorials. Build robust Retrieval-Augmented Generation pipelines using Knowledge Bases for Amazon Bedrock. Learn to ingest data from S3, configure vector stores (OpenSearch Serverless), and implement advanced retrieval patterns like Hybrid Search and Cohere Rerank to eliminate hallucinations. Autonomous Agents: Transform passive chatbots into active workers.1 Design Bedrock Agents that can reason, plan, and execute tasks by calling your own Lambda functions. Master the art of writing OpenAPI schemas that teach the AI how to use your internal APIs. Safety & Governance: Deploy with confidence using Guardrails for Amazon Bedrock . Implement PII redaction, content filtering, and denied topic policies to ensure your application adheres to strict enterprise compliance standards. Observability & Operations: Stop flying blind. Set up CloudWatch dashboards to track token usage, latency, and costs.2 Learn to audit every interaction with CloudTrail and optimize your spend using Provisioned Throughput and caching strategies. Why This Book? 100% Python & Boto3: No pseudo-code. Every chapter is packed with executable scripts you can drop directly into your IDE. Architectural Focus: Learn the patterns that matter-state management with DynamoDB, event-driven ingestion, and the "Human-in-the-Loop" workflow. Future-Proof Design: Discover how to build "Model Gateways" that allow you to swap underlying models instantly as new versions are released. Whether you are a backend engineer tasked with adding AI to a legacy app, or a cloud architect designing a new intelligent platform, this book provides the blueprints you need. Master the stack. Control the cost. Deploy the future. Scroll up and grab your copy today to start building on Amazon Bedrock.

Leland T. Saito, "Building Downtown Los Angeles: The Politics of Race and Place in Urban America"
English | ISBN: 1503632520 | 2022 | 266 pages | AZW3 | 3 MB
From the 1970s on, Los Angeles was transformed into a center for entertainment, consumption, and commerce for the affluent. Mirroring the urban development trend across the nation, new construction led to the displacement of low-income and working-class racial minorities, as city officials targeted these neighborhoods for demolition in order to spur economic growth and bring in affluent residents. Responding to the displacement, there emerged a coalition of unions, community organizers, and faith-based groups advocating for policy change. In Building Downtown Los Angeles Leland Saito traces these two parallel trends through specific construction projects and the backlash they provoked. He uses these events to theorize the past and present processes of racial formation and the racialization of place, drawing new insights on the relationships between race, place, and policy. Saito brings to bear the importance of historical events on contemporary processes of gentrification and integrates the fluidity of racial categories into his analysis. He explores these forces in action, as buyers and entrepreneurs meet in the real estate marketplace, carrying with them a fraught history of exclusion and vast disparities in wealth among racial groups.

Building Command-Line Tools in Python : A Step-by-Step Guide with Examples
English | 20 Nov. 2025 | ASIN: B0G35DM946 | 329 pages | Epub | 460.98 KB
Unlock the Power of Command-Line Tools with Python - Your Essential Guide to Building Efficient, Automation-Driven Applications! If you've ever found yourself repeating tedious terminal commands or dreaming of a faster way to automate everyday tasks, this step-by-step guide is your gateway to mastery. Building Command-Line Tools in Python demystifies the art of creating robust, user-friendly CLIs that streamline workflows for developers, sysadmins, and power users alike. Written by Charles SPARK, this hands-on book takes you from foundational concepts to advanced techniques, all backed by practical examples and real-world applications. Whether you're a beginner dipping into automation or a seasoned coder refining your toolkit, you'll discover Python's simplicity and versatility in CLI development. Learn to harness built-in modules like argparse, elevate your UX with frameworks such as Click and Typer, and integrate APIs, files, and async features for high-performance tools. Key topics include: Introduction to CLIs: History, benefits, and setting up your environment Argument parsing, error handling, and interactive prompts Modern frameworks for rich output, validation, and type hints Integrating external libraries, APIs, and asynchronous operations Testing with Pytest, debugging, and end-to-end strategies Packaging executables, documentation, and distribution best practices Advanced configuration, performance optimization, security, and maintainability Real-world case studies: DevOps automation, data pipelines, and system monitoring With clear explanations, code snippets, and best practices, you'll build tools that save time and scale effortlessly. Dive in and transform your command-line skills today - perfect for anyone ready to automate the mundane and empower the extraordinary!

Building Autonomous AI Agents with LangGraph: A Developer's Guide by Ajit Singh
English | August 30, 2025 | ISBN: N/A | ASIN: B0FPBLC345 | 355 pages | EPUB | 0.53 Mb
This book, "Building Autonomous AI Agents with LangGraph: A Developer's Guide," is born from a desire to demystify the art and science of agent creation. My goal is to provide a clear, practical, and hands-on journey for students and developers alike. We will start from the absolute fundamentals-what an agent is and why we need them-and progressively build our way up to deploying a sophisticated, autonomous research assistant. Every concept is introduced with a clear purpose, immediately followed by practical code examples, and contextualized with real-world scenarios. I believe that the best way to understand an AI agent is to build one, debug it, and watch it work.

Building Agentic AI Systems: Create intelligent, autonomous AI agents that can reason, plan, and adapt
English | April 21, 2025 | ASIN: B0F22KNJ7C | 507 pages | EPUB (True) | 4.71 MB
Master the art of building AI agents with large language models using the coordinator, worker, and delegator approach for orchestrating complex AI systems Free with your book: PDF Copy, AI Assistant, and Next-Gen Reader Key Features Understand the foundations and advanced techniques of building intelligent, autonomous AI agents Learn advanced techniques for reflection, introspection, tool use, planning, and collaboration in agentic systems Explore crucial aspects of trust, safety, and ethics in AI agent development and applications Book Description Gain unparalleled insights into the future of AI autonomy with this comprehensive guide to designing and deploying autonomous AI agents that leverage generative AI (GenAI) to plan, reason, and act. Written by industry-leading AI architects and recognized experts shaping global AI standards and building real-world enterprise AI solutions, it explores the fundamentals of agentic systems, detailing how AI agents operate independently, make decisions, and leverage tools to accomplish complex tasks. Starting with the foundations of GenAI and agentic architectures, you'll explore decision-making frameworks, self-improvement mechanisms, and adaptability. The book covers advanced design techniques, such as multi-step planning, tool integration, and the coordinator, worker, and delegator approach for scalable AI agents. Beyond design, it addresses critical aspects of trust, safety, and ethics, ensuring AI systems align with human values and operate transparently. Real-world applications illustrate how agentic AI transforms industries such as automation, finance, and healthcare. With deep insights into AI frameworks, prompt engineering, and multi-agent collaboration, this book equips you to build next-generation adaptive, scalable AI agents that go beyond simple task execution and act with minimal human intervention. What you will learn Master the core principles of GenAI and agentic systems Understand how AI agents operate, reason, and adapt in dynamic environments Enable AI agents to analyze their own actions and improvise Implement systems where AI agents can leverage external tools and plan complex tasks Apply methods to enhance transparency, accountability, and reliability in AI Explore real-world implementations of AI agents across industries Who this book is for This book is ideal for AI developers, machine learning engineers, and software architects who want to advance their skills in building intelligent, autonomous agents. It's perfect for professionals with a strong foundation in machine learning and programming, particularly those familiar with Python and large language models. While prior experience with generative AI is beneficial, the book covers foundational concepts for those new to agentic systems. Table of Contents Fundamentals of Generative AI Principles of Agentic Systems Essential Components of Intelligent Agents Reflection and Introspection in Agents Enabling Tool Use and Planning in Agents Exploring the Coordinator, Worker, and Delegator Approach Effective Agentic System Design Techniques Building Trust in Generative AI Systems Managing Safety and Ethical Considerations Common Use Cases and Applications Conclusion and Future Outlook

Building Learning Communities with Character How to Integrate Academic, Social, and Emotional Learning

Building Income with ChatGPT Practical AI Applications for Modern Entrepreneurs

Building Generative AI on AWS Bedrock Develop LLM Applications with Python, RAG, and Agents

Building Games with Swift

Building Event-Driven Microservices Leveraging Organizational Data at Scale

Building Downtown Los Angeles The Politics of Race and Place in Urban America

Building Command-Line Tools in Python A Step-by-Step Guide with Examples

Building Autonomous AI Agents with LangGraph A Developer's Guide

Building Applications using AI Agents

Building Agentic AI Systems Create intelligent, autonomous AI agents that can reason, plan, and adapt