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Total Size:
3.0 MB
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7A579D180AF0CF69639DCE7592781DF683F172CA
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April 20, 2026, 2:53 p.m.
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(Last updated: April 20, 2026, 2:59 p.m.)
| File | Size |
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| Van Der Post H. AI Agents with Python. Build Autonomous Systems...2025.pdf | 3.0 MB |
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780.5 MB
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2025-03-08
| Uploaded by CorsaroNero | Size 780.5 MB | Health [ 9 /0 ] | Added 2025-03-08 |
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779.7 MB
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2025-03-08
| Uploaded by CorsaroNero | Size 779.7 MB | Health [ 17 /1 ] | Added 2025-03-08 |
NOTE
SOURCE: Van Der Post H. AI Agents with Python. Build Autonomous Systems...2025
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COVER
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MEDIAINFO
Textbook in PDF format The future of AI isn’t just models — it’s agents. Systems that can observe, reason, make decisions, and take action in dynamic environments. In AI Agents with Python, you'll learn how to go beyond isolated models and build fully autonomous systems that combine logic, memory, learning, and action — all in real-world applications. Whether you're a developer, data scientist, or an AI-curious builder, this book walks you through the core components of intelligent agent design — and shows you how to bring them to life with Python. The effectiveness of autonomous systems largely depends on their capacity to learn from interactions with their environment, a process fundamentally driven by reward systems. Central to reinforcement learning, these systems utilize feedback mechanisms that guide behavior based on the consequences of their actions. This learning approach closely resembles how humans and animals acquire knowledge—through trial and error, adjusting strategies in response to rewards or penalties. A reward system assigns a scalar value that assesses the quality of an agent's actions in a specific state. The design of this reward function is crucial; it must be thoughtfully crafted to encourage agents to adopt the desired behaviors. For example, consider a robot navigating a maze. When it successfully reaches the exit, it receives a positive reward, reinforcing the sequence of actions that led to this successful outcome. Conversely, if it collides with a wall, a negative reward serves to discourage that particular move. To illustrate this concept, let’s look at a simple Python implementation that simulates an agent learning to navigate a grid environment using Q-learning—a widely used reinforcement learning algorithm. Inside, you’ll build and deploy agents that Use LLMs, APIs, and structured data to reason and respond Incorporate memory, tools, and recursive planning Interface with real-world systems (files, browsers, databases) Perform multi-step tasks autonomously using frameworks like LangChain, Auto-GPT, and CrewAI Integrate reinforcement learning and feedback loops Apply to use cases like customer service, trading, research, automation, and personal assistants Packed with code examples, architectural blueprints, and real-world use cases, this book gives you everything you need to start building intelligent, useful agents from scratch. Build smarter systems. Automate complex work. Create the future. Preface Definition and History of AI Agents Understanding the Basics of Machine Learning Deep Dive into Neural Networks Creating Intelligent Agents Reinforcement Learning for Autonomous Agents Natural Language Processing in AI Agents Decision-Making in Autonomous Systems Learning in Multi-Agent Systems Robotics and Automation with AI Agents AI Agents for Game Development Security and Privacy in AI Systems Distributed AI Systems Evaluating and Benchmarking AI Agents Advanced Topics in AI Agent Development The Future of AI and Autonomous Systems
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