Скачать или смотреть How Neural Networks Really Work - From Logistic to Piecewise Linear Regression [Lecture 5.4]

"Can we understand the black box neural networks? Why neural networks often are just composed of linear sections."

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Part 1: Why Neural Networks for Machine Learning?
   • Why Neural Networks for Machine Learning? ...  

Part 2: Building Neural Networks - Neuron, Single Layer Perceptron, Multi Layer Perceptron
   • Building Neural Networks - Neuron, Single ...  

Part 3: Activation Function of Neural Networks - Step, Sigmoid, Tanh, ReLU, LeakyReLU, Softmax [
   • Activation Function of Neural Networks - S...  

Part 4: How Neural Networks Really Work - From Logistic to Piecewise Linear Regression
   • Activation Function of Neural Networks - S...  

Part 5: Delta Rule for Neural Network Training as Basis for Backpropagation
   • Delta Rule for Neural Network Training as ...  

Part 6: Derive Backpropagation Algorithm for Neural Network Training
   • Derive Backpropagation Algorithm for Neura...  

Part 7: Gradient Based Training of Neural Networks
   • Gradient Based Training of Neural Networks...  

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We consider different neural network structures from a single neuron to deeper neural networks. The effect of the number of neurons and the number of layers to the neural network output is examined. Further, we consider a single as well as multiple input variables. Feedforward neural networks can approximate functions by a piecewise linear regression. This interpretation is visualized with many examples.