Novedades del último mes en eBooks de Inteligencia artificial

Build, Scale and Optimize Cutting-Edge NLP with Llama for Next Gen AI. Key Features Explore Llamas evolution and innovations for next-gen NLP. Implement real-world NLP tasks with step-by-step examples. Fine-tune, optimize, and deploy Llama at enterprise scale. Book Description Llama models have rapidly emerged as a cornerstone in natural language processing, redefining how AI systems understand and generate human language. From their efficient architecture to the cutting-edge advancements in Llama 4, these models enable enterprises, researchers, and developers to build powerful, scalable, and responsible NLP solutions. This book, Ultimate Llama for Natural Language Processing (NLP), takes you on a structured journey through the evolution and applications of Llama. It begins with the foundations of the Llama series and its architecture, before progressing to core NLP tasks such as classification, summarization, sentiment analysis, and conversational AI. Subsequent chapters cover fine-tuning, transfer learning, optimization, and deployment at enterprise scale, with practical insights into real-world industry use cases. The book also addresses troubleshooting, ethical AI, and the future of multimodal and sparse Mixture-of-Experts models. Thus, by the end, readers will be well-equipped to train, adapt, and deploy Llama models across domains such as healthcare, finance, and customer engagement. What you will learn Understand Llamas evolution, architecture, and unique innovations in NLP. Implement core NLP tasks like classification, NER, and summarization. Fine-tune Llama for custom domains using advanced transfer learning. Optimize inference speed, and deploy Llama models at enterprise scale. Troubleshoot, monitor, and continuously improve Llama model performance. Apply Llama 4 to real-world industry use cases and multimodal AI.

Build Real-time IoT Solutions Using Python, Edge, and Docker Tools. Key Features Explore real-world IoT use-cases using Python and edge computing. Hands-on projects on embedded devices like Raspberry Pi and Jetson Nano. Learn edge-cloud integration, Docker, and protocols such as MQTT and CoAP. Covers security, streaming data, and future edge computing trends. Book Description The Internet of Things (IoT) is reshaping the way the world collects, processes, and responds to datafrom smart homes and wearables to autonomous vehicles and industrial automation. As the demand for fast, secure, and intelligent data handling grows, edge computing becomes a key enabler, pushing computation closer to the source and reducing latency, cloud dependency, and security risks. Practical IoT Edge Computing with Python equips readers with a complete, hands-on roadmap to build robust, real-time IoT systems, using edge devices and Python. You will begin by understanding the core concepts of IoT, and the limitations of traditional cloud-based models. Thus, step by step, you will move through building an IoT data pipeline, integrating edge and cloud, and deploying on devices like Raspberry Pi and Jetson Nano, as well as working with protocols such as MQTT and CoAP. You will also gain practical experience with data preprocessing, edge intelligence, containerization (Docker/Kubernetes), and security measures like blockchain. Each chapter builds your confidence to design scalable, secure, and responsive IoT systems. Hence, whether you are a student, developer, or industry professional, this book offers the tools and knowledge to turn IoT concepts into fully functioning edge solutions. What you will learn Understand edge vs. cloud computing in IoT system architectures. Build IoT data pipelines including pre-processing and analysis steps. Deploy Python-based data processing on embedded edge platforms. Use networking protocols (MQTT, CoAP and AMQP) in edge devices. Containerize models with Docker/Kubernetes for edge deployment. Secure IoT systems with blockchain, and understand privacy challenges.

Harness Genetic Algorithms to Build the Next Generation of Adaptive AI. Key Features Step-by-step tutorials on Genetic Algorithms, using PyGAD and DEAP. Real-world Genetic Algorithm applications in ML, DL, NLP, CV, and RL. Advanced coverage of evolutionary and metaheuristic algorithms. Integration of Genetic Algorithms with generative and agent-based AI systems. Book Description Genetic Algorithms (GAs) are nature-inspired optimization tools that help AI systems adapt, improve, and solve complex problems efficiently. Ultimate Genetic Algorithms with Python explains elaborately the fundamentals of GAs to practical, Python-based implementation, using PyGAD and DEAP. The book starts with a solid foundation, explaining how evolutionary principles can be applied to optimization tasks, search problems, and model improvement. You will also explore GA applications across multiple AI domains: optimizing machine learning workflows, evolving neural network architectures in deep learning, enhancing feature selection in NLP, improving performance in computer vision, and guiding exploration strategies in reinforcement learning. Each application chapter includes step-by-step coding examples, performance comparisons, and tuning techniques. The later sections focus on advanced metaheuristics, swarm intelligence, and integrating GAs with generative and agent-based AI systems. You will also learn how to design self-evolving, multi-agent frameworks, leverage swarm-based methods, and connect GAs to next-gen AI architectures such as Model Context Protocols (MCP). What you will learn Master the fundamentals and components of Genetic Algorithms. Implement GAs in Python, using PyGAD, DEAP, and PyTorch. Apply GAs for optimization, feature selection, and neural architecture search. Enhance AI workflows in ML, DL, NLP, CV, and RL with GAs. Explore metaheuristic and swarm-based algorithms for complex problem-solving. Integrate GAs into generative, multi-agent, and self-evolving AI systems.