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The shifting identity of the Atari 8-bit family, and how a single platform can provoke a universal debate about the purpose of computing.Throughout the late 1970s and early 1980s, Atari became synonymous with arcade gaming, producing iconic games such as Pong and Asteroids. Yet the companys ambitions extended beyond games and consoles. In 1979, Atari launched its first 8-bit computera platform initially designed as a console before its release into the market as a computer. This ambiguous identity produced a curious marketing campaign that downplayed the computers main selling point as a gaming machine; within three years, Atari had switched tactics and repackaged the computer as a console. In At the End of the Rainbow, Pawel Grabarczyk examines how a device that had to shed its image as a glorified console later became a console accused of being a computer in disguise, resulting in its varied reception across the globe.As the Atari 8-bit platform faded into obsolescence in the West, it found unexpected success in other parts of the worldChile, Eastern Europe, and especially in Socialist Poland, where it led the digital revolution and kickstarted the domestic gaming industry. Grabarczyk traces these divergent trajectories across capitalist and socialist contexts and explores how the platforms fate sparked a larger debate about the boundaries between console and computer, between playful and serious usersand about the very purpose of computing itself.

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A unique approach to building trustworthy AI systems with open-source tools, combining hands-on projects, real-world relevance, and a focus on innovation and collaboration.Build Your Own AI is your practical, creative guide to building ethical and impactful AI systems using todays most powerful open-source tools.Clear, jargon-free explanations will walk you through libraries like PyTorch, TensorFlow, and Keras as you build classifiers, character predictors, and digit recognizers. Youll also explore advanced topics like MLOps, AI security, and responsible AI development, putting tools such as Hugging Face, LangChain, CrewAI, ANOVA, and SHAP into action.Through hands-on projects like predicting superhero feats, generating story plotlines, and crafting and detecting deepfakes, youll combine technical insight with imaginative exploration. Youll even build a trivia game powered by agentic bots using top-tier models like LLaMA, Whisper, and Stable Diffusion.By the end, youll know how to build and deploy AI systems and be ready to confidently join the global open-source AI movement. Walk through that door today with Build Your Own AI.

Build intelligent applicationsno data science degree required.Your boss is pitching new AI features. Your team is buzzing about MCP servers. Job postings are asking for AI experience with RAG, vector databases, fine-tuning, and agents. You can feel the excitement. You see the potential. You may be wondering how to get started in AI without a data science degree. Youre in the right place.The Developers Guide to AI gives working developers a practical path through the terminology, tools, and implementation patterns that matter. It shows you how to build with AI using the tools you already know: JavaScript, Python, APIs, SDKs, and databases.By the end of this book, youll know how to: Call LLM APIs and stream intelligent responses directly to your UI. Engineer prompts that produce reliable, production-ready results. Build RAG pipelines using vector databases to give AI access to your private data. Fine-tune models with LoRA for specialized tasks like classification. Deploy AI agents using tool-calling and the Model Context Protocol (MCP) to reason and act inside real workflows. LLMs, RAG, LoRA, MCP, embeddings, and agents are not just intimidating buzzwords. They are the building blocks for the next generation of software.Grab your code editor, bring your engineering instincts, and lets build whats next!

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Vous pensiez que les programmeurs étaient des espèces de magiciens venus dun pays imaginaire et dotés de pouvoir magiques ? Croyez-le ou non, mais ce livre va vous montrer quun humain ordinaire peut

A comprehensive, cutting-edge introduction to neuroevolution that equips AI professionals and advanced students to harness evolutionary creativity for innovative AI solutions.Neuroevolutionthe fusion of neural networks with evolutionary algorithmsis a powerful approach to AI model design. Providing a comprehensive, cutting-edge introduction to the field, this textbook methodically moves from fundamental principles to state-of-the-art techniques, highlighting how evolutionary processes can creatively design neural network architectures and behaviors beyond what conventional methods achieve.The book starts with core concepts of evolutionary computation and basic neuroevolution examples, followed by key design considerations like genetic encodings and the critical role of diversity. It then covers evolving intelligent agents, from low-level control through intelligent behavior to high-level multi-agent decision-making strategies. Next, it discusses integrating neuroevolution with other modern AI methods such as large language models and reinforcement learning. Finally, it offers insights into biological evolution, examining evolved neural structures, developmental processes, coevolution of bodies and brains, and the evolution of behaviors and language. Written by pioneers of the field, this timely book shows how the creativity of evolution can be harnessed in AI, automating the discovery of novel neural network solutions that improve adaptability and performance in complex, uncertain environments. Provides a structured, step-by-step path from basics to advanced topics Details essential mathematical and conceptual foundations Unifies decades of research in one place, explaining key algorithms with illustrative examples Explores how neuroevolution interfaces with contemporary AI advances Includes extensive real-life case studies across robotics, games, art, and decision-making Features a comprehensive online supplement with demos, tutorials, lecture slides and videos, exercises and code

A project-driven guide to designing, training, and deploying artificial intelligence directly on embedded hardware, showing how to build intelligent, autonomous systems under real-world constraints.If you already know your way around a microcontroller and want to add embedded AI to itor you work in ML and youre ready to get your hands on real hardwarethis book is for you. It covers the full embedded AI stack, from circuit design and custom PCB fabrication through sensor fusion and signal processing to on-device inference.Youll learn how to wire the sensor, condition the signal, fuse IMU data using complementary filters, Madgwick, Mahony, and Kalman filters, deploy decision trees that run inside the sensor itself, and figure out why your tensor arena is the wrong size. Along the way, youll tackle exploratory data analysis, model quantization, and the debugging realities that documentation never mentionslike what to do when the firmware uploader is fragile and your breadboard connections are dodgy.Working on Arduino (UNO R3 and R4, Nano 33 BLE Sense, Nicla Vision, Nicla Voice), Raspberry Pi Pico 2, and ST evaluation boards, youll build 25 complete projects, including: Signal generator using PIO and DMA on the Raspberry Pi Pico Battery state-of-charge prediction with Gaussian Process Regression Person detection using CNNs on the Nicla Vision Orientation detection using finite state machines running on-sensor Sensor fusion filter comparison across four IMUs with static angle testing Robot arm anomaly detection with decision trees on the ISM330BX machine learning core Real-time audio noise suppression using a GRU neural network on the Pico 2 AI MIDI synthesizer with GAN-generated music, capacitive touch keyboard, VS1053b hardware synth, and procedural composition with Markov chains--all on custom PCBs Hot word detection on the Nicla Voice using Edge Impulse Battery monitor and logging shield with BQ24075 charger, fuel gauge, and programmable discharge load Five custom PCBs are designed and built across the projects, with Gerber files and schematics provided. All code and hardware designs are open source under the MIT License.This is embedded AI as a complete engineering disciplinesensors, circuits, signal processing, machine learning, and firmwarenot a software shortcut.

A practical, experiment-driven introduction to deep learning that shows programmers how modern neural networks actually work by running, inspecting, and reasoning about real models, from foundational concepts through large language models.How Deep Learning Works is for programmers who want to understand deep learning, not just use it.Rather than starting with heavy math or abstract theory, this book takes an experiment-first approach. Each chapter walks readers through carefully designed experiments using real deep learning toolkits, guiding them step by step from running code to understanding why models behave the way they do. A consistent structureoverview, execution, code review, and discussionkeeps readers oriented and focused on building intuition, not memorizing APIs.Beginning with fundamental ideas like classification and neural networks, the book steadily builds toward modern techniques, including transfer learning, zero-shot and few-shot models, and hands-on experiments with large language models. Along the way, readers learn how to spot failure modes, reason about trade-offs, and adapt existing tools to new problems.The result is a clear, software-centric explanation of deep learning that helps working programmers move beyond copy-paste ML and develop real understanding they can apply in their own projects.

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