AI Foundations for Absolute Beginners: Modeling Neural Networks

Описание: Welcome to Episode 1 of our AI Foundation Series! 🧠⚡

In this video, we dive into the fundamentals of artificial neural networks, starting with the most basic building block: the neuron. Discover how biological neurons inspired AI, learn about key concepts like the all-or-none principle, activation thresholds, and weighted inputs, and see how these ideas translate into mathematical models powering modern machine learning.

Whether you're a beginner or looking to solidify your understanding, this bottom-up approach will help you grasp AI one concept at a time. Don’t forget to like, subscribe, and hit the bell icon for more!


⏳ Chapters
0:00 - Intro
00:41 - Consciousness Built From Simplicity
01:29 - Properties of Neurons and Neural Networks
03:40 - Single Neuron Input/Output Structure
04:01 - All-or-None Behavior
04:31 - Interpreting The Output of A Neuron
05:23 - Triggering Mechanism of A Neuron
06:11 - Internal Parameters of A Neuron: NAT & NMO
07:34 - Neurons As Universal Computing Tools
08:10 - Modeling Stage I: NMO as A Function
09:31 - Equivalency of Single Neuron Systems: Linear Chains
10:18 - Real-World Example: Inputs as Objective Measurements
11:27 - Subjective Evaluation Property of A Neuron
11:55 - Condensing Multiple Objective Inputs to A Single Number
12:27 - Weighted Averaging
13:06 - Optimal Weight Selection Strategies
14:04 - Modeling Stage II: Input to Stage I and Function Selection
17:35 - Conclusion & Outro


🎯 Key Topics Covered
Biological vs. Artificial Neurons
Threshold Logic & Binary Outputs
Mathematical Modeling (NAT, NMO)
Weighted Inputs & Bias Adjustment
Activation Functions (Linear, ReLU, Sigmoid)
Practical Examples & Calculations

📌 Hashtags
#NeuralNetworks #MachineLearning #AIForBeginners #DeepLearning #ArtificialIntelligence