[MULTI] Artificial Neural Network For Beginners

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Artificial Neural Network For Beginners
Published 1/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 2m | Size: 978 MB​

Demystifying Artificial Neural Networks for absolute beginners
What you'll learn
Understand the basics of Artificial Neural Networks (ANNs).
Learn how biological neurons inspire artificial networks.
Explore key ANN architectures and learning mechanisms.
Build intuition to move toward machine learning and AI topics.
Requirements
Basic knowledge of mathematics (algebra, functions, simple calculus).
Description
Artificial Neural Networks (ANNs) are at the core of modern Artificial Intelligence. This beginner-friendly course is designed to introduce you to the concepts, structures, and applications of ANNs without the need for any programming knowledge. Using intuitive explanations, real-world examples, and clear visualizations, you'll learn how artificial neurons work, how networks are trained, and where they're applied in today's world.By the end of this course, you'll have a solid understanding of how neural networks function and the confidence to explore more advanced AI and deep learning topics.What you'll learnUnderstand the fundamentals of Artificial Neural Networks (ANNs).Learn how biological neurons inspire artificial networks.Explore key ANN architectures and learning mechanisms.Build intuition to move toward machine learning and AI topics.Who this course is for:Beginners with no programming background.Students wanting to understand ANN concepts clearly.Non-technical learners interested in AI and machine learning.Professionals seeking AI knowledge without coding complexity.Course CurriculumSection 1: Introduction to Neural NetworksBiological vs. Artificial NeuronsReal-world applicationsSection 2: Fundamentals of Artificial NeuronsStructure of a neuronActivation functionsSimple examplesSection 3: Architecture of Neural NetworksSingle-layer and multi-layer perceptron'sForward propagationSection 4: Learning in Neural NetworksTraining and loss functionsGradient descent & backpropagation (conceptual)Section 5: Types of Neural NetworksFeedforward, CNNs, RNNsOther architectures overview
Who this course is for
Students wanting to understand ANN concepts clearly.
Non-technical learners interested in AI and machine learning.
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