{"product_id":"foundations-of-artificial-intelligence-agent-systems-neural-networks-and-deep-learning-for-beginners","title":"Foundations of Artificial Intelligence: Agent Systems, Neural Networks, and Deep Learning for Beginners","description":"\u003cdiv\u003e\n\u003cp\u003eFoundations of Artificial Intelligence: Agent Systems, Neural Networks, and Deep Learning for Beginners is your friendly doorway into AI. This beginner-friendly video course covers agent systems, neural networks, deep learning, machine learning, and computer vision, with a clear historical perspective to show how AI arrived here—and why it still matters.\u003c\/p\u003e \u003ch2\u003eKey Features \u0026amp; Design\u003c\/h2\u003e\n\u003cul\u003e \u003cli\u003e\n\u003cb\u003eBeginner-friendly video course\u003c\/b\u003e covering agent systems, neural networks, deep learning, machine learning, and computer vision.\u003c\/li\u003e \u003cli\u003e\n\u003cb\u003eHistorical Perspective\u003c\/b\u003e helps you understand AI's evolution—from the earliest problem solvers to modern intelligent systems.\u003c\/li\u003e \u003cli\u003e\n\u003cb\u003eStructured topics\u003c\/b\u003e aligned with the course outline: Introduction and Historical Background of AI; The General Problem Solver; Expert Systems; Neural Networks; Machine Learning: Deep Learning \u0026amp; Computer Vision.\u003c\/li\u003e \u003cli\u003e\n\u003cb\u003eNo prior knowledge required\u003c\/b\u003e (Requirements: No prior knowledge of AI required).\u003c\/li\u003e \u003cli\u003e\n\u003cb\u003eCore concepts made practical\u003c\/b\u003e including forward and backward chaining, probabilities in expert systems, neural signals, perceptron, layers of deep learning networks, and Convolutional Neural Networks (CNNs).\u003c\/li\u003e \u003cli\u003e\n\u003cb\u003eReal-world applications\u003c\/b\u003e with case studies and examples in speech and image recognition, plus a case study: Optimizing potato harvesting.\u003c\/li\u003e \u003cli\u003e\n\u003cb\u003eWhat you’ll learn\u003c\/b\u003e— a clear, actionable outcome set: the structure and design of modern AI systems; differences between strong and weak AI; fundamentals of deep learning and machine learning; problem structures and solving techniques; machine vision and computer vision basics.\u003c\/li\u003e\n\u003c\/ul\u003e \u003ch3\u003eWhat You’ll Learn\u003c\/h3\u003e\n\u003cul\u003e \u003cli\u003eThe structure and design of modern AI systems\u003c\/li\u003e \u003cli\u003eDifferences between strong and weak AI\u003c\/li\u003e \u003cli\u003eFundamentals of deep learning and machine learning\u003c\/li\u003e \u003cli\u003eProblem structures and solving techniques\u003c\/li\u003e \u003cli\u003eForward and backward chaining in AI logic\u003c\/li\u003e \u003cli\u003eProbabilities in expert systems\u003c\/li\u003e \u003cli\u003eThe function of human neurons and their digital equivalents\u003c\/li\u003e \u003cli\u003eLayers of deep learning networks\u003c\/li\u003e \u003cli\u003eMachine vision and computer vision basics\u003c\/li\u003e\n\u003c\/ul\u003e \u003ch3\u003eCourse Topics \u0026amp; Lessons\u003c\/h3\u003e\n\u003cul\u003e \u003cli\u003e\n\u003cb\u003eI. Introduction and Historical Background\u003c\/b\u003e — Philosophical perspective: What is AI? Strong AI vs. Weak AI; The Turing Test; The origins of AI; The era of high expectations and its reality check; How machines learn; Distributed systems in AI; Deep Learning, Machine Learning, and Natural Language Processing.\u003c\/li\u003e \u003cli\u003e\n\u003cb\u003eII. The General Problem Solver\u003c\/b\u003e — Logical Theorist: The first proof program; Human problem-solving examples (Simon); Analyzing the structure of a problem; foundational AI techniques shaping early optimism.\u003c\/li\u003e \u003cli\u003e\n\u003cb\u003eIII. Expert Systems\u003c\/b\u003e — Factual and heuristic knowledge; Frames, slots, and fillers; Forward and backward chaining; The MYCIN program; Probabilities in expert systems; Real-world application: Calculating the probability of hairline cracks.\u003c\/li\u003e \u003cli\u003e\n\u003cb\u003eIV. Neural Networks\u003c\/b\u003e — Understanding the human neuron; Neuron signal processing; The perceptron; early approaches and the missing links that led to breakthroughs in neural network development.\u003c\/li\u003e \u003cli\u003e\n\u003cb\u003eV. Machine Learning: Deep Learning \u0026amp; Computer Vision\u003c\/b\u003e — Case study: Optimizing potato harvesting; The birth of deep learning; Layers in deep learning networks; Machine vision and computer vision; Convolutional Neural Networks (CNNs); multi-agent systems and real-world AI applications such as speech and image recognition.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e","brand":"MyHackerTech","offers":[{"title":"Default Title","offer_id":42006394667095,"sku":"sku-47049886564592","price":40.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0646\/0696\/1751\/files\/Untitled_67.jpg?v=1759609720","url":"https:\/\/darkbytegear.com\/products\/foundations-of-artificial-intelligence-agent-systems-neural-networks-and-deep-learning-for-beginners","provider":"DarkByteGear","version":"1.0","type":"link"}