Скачать или смотреть The Anatomy of an Artificial Mind: Neural Networks, Perceptron's Flaw, and Deep Learning Explained.

Dive deep into the architecture of modern AI with this comprehensive deconstruction of the Neural Network. We go beyond the buzzwords to explore the core mathematical mechanics, from the biological inspiration to the sophisticated non-linear functions that power Deep Learning.
Learn why the early AI model, the Perceptron, led to the 'AI Winter,' and discover the two mathematical secrets—Bias and Non-Linearity—that allowed the modern neural network to solve complex, real-world problems and unlock the current age of Artificial Intelligence. This video is essential viewing for anyone who wants to truly understand how AI interprets the world.

In this video, we deconstruct:
The Neural Network's Structure: Input, Hidden, and Output Layers.
The Neuron's Evolution: From the Biological blueprint to the Artificial model.
The Perceptron's History: The first model and its simple, binary logic.
The Fatal Flaw: Why single-layer networks failed with non-linearly separable problems (like XOR).
The Collapse Puzzle: The mathematical proof that a deep, linear network is no better than a single neuron.
The Secret Ingredient: How non-linear Activation Functions (like ReLU) prevent the collapse and enable powerful learning.
Activation Functions Deep Dive: A look at ReLU, Leaky ReLU, Sigmoid, and Tanh.
The Final Step: Using the Softmax function to translate raw scores into meaningful confidence probabilities.




Keywords & Topics:
#NeuralNetwork #DeepLearning #Perceptron #AIExplained #MachineLearning #NonLinearity #ActivationFunction #Softmax #ForwardPropagation #HowAIWorks
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Neural Network, Deep Learning, How AI Works, Perceptron, Activation Function, Machine Learning, Non-Linearity, ReLU, Forward Propagation, Artificial Intelligence