Lisp-the language that birthed AI-is making a powerful comeback, perfectly suited for the demands of Explainable AI , Meta-Learning , and self-modifying systems . Its unique design, which unifies code and data (homoiconicity) and features an unmatched macro system , allows you to sculpt algorithms rather than just assemble them. This approach fosters creativity and adaptability, bridging the gap between classical symbolic reasoning and modern neural networks. Lisp for Machine Learning is the definitive guide for ...
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Lisp-the language that birthed AI-is making a powerful comeback, perfectly suited for the demands of Explainable AI , Meta-Learning , and self-modifying systems . Its unique design, which unifies code and data (homoiconicity) and features an unmatched macro system , allows you to sculpt algorithms rather than just assemble them. This approach fosters creativity and adaptability, bridging the gap between classical symbolic reasoning and modern neural networks. Lisp for Machine Learning is the definitive guide for engineers and data scientists seeking to build intelligent, scalable, and transparent AI systems using Common Lisp, Clojure, and Scheme . You'll master the functional and symbolic core of Lisp to create models that not only learn from data but can also reason about and explain their decisions . The book covers everything from implementing foundational algorithms (Regression, Decision Trees) and deep learning networks from scratch, to building neuro-symbolic hybrids and deploying high-performance services on the JVM. What's Inside The book progresses from foundational principles to advanced production techniques, ensuring you can apply Lisp's strengths at every stage of the AI lifecycle: Lisp's Foundational Role (Chapter 1, 8): Understand how Lisp's symbolic processing supports Knowledge Representation and the development of Hybrid Neuro-Symbolic Systems for robust, interpretable results. Core Algorithms from Scratch (Chapter 6, 7): Implement Linear Regression , Gradient Descent , and Feedforward Neural Networks in a clear, functional style that mirrors mathematical formulas. Adaptive Systems (Chapter 9, 12): Master the design of Reinforcement Learning Agents and explore Self-Modifying Code techniques that enable continuous Meta-Learning and runtime optimization. Scalable Production (Chapter 3, 11): Learn to integrate Lisp with Python and JVM ecosystems (e.g., Spark, Deeplearning4j), use Clojure concurrency for large datasets, and tune performance for low-latency cloud deployment. Are you a Data Scientist curious about model interpretability, or an experienced Lisp developer eager to tackle neural networks and scalable pipelines? This book is for you. If you yearn for the clarity and adaptability missing in monolithic frameworks, you'll gain the mental flexibility to move seamlessly between symbolic reasoning and statistical learning , creating intelligent systems that truly think. Stop debugging opaque models. Master Lisp, build transparent intelligence, and revolutionize your AI workflow. Add this book to your toolkit today!
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Add this copy of Lisp for Machine Learning: Build Intelligent Systems to cart. $18.51, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2025 by Independently Published.
Add this copy of Lisp for Machine Learning to cart. $26.36, new condition, Sold by Paperbackshop rated 4.0 out of 5 stars, ships from Bensenville, IL, UNITED STATES, published 2025 by Amazon Digital Services LLC-Kdp.
