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Deep Learning A-Z™: Artificial Neural Networks (ANN) - Backpropagation

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Kirill Eremenko

Deep Learning A-Z™: Artificial Neural Networks (ANN) - Backpropagation

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© SuperDataScienceDeep Learning A-Z
© SuperDataScienceDeep Learning A-Z Image Source: neuralnetworksanddeeplearning.com Forward PropagationBackpropagation
© SuperDataScienceDeep Learning A-Z Neural Networks and Deep Learning Michael Nielsen (2015) Link: http://neuralnetworksanddeeplearning.com/chap2.html Additional Reading:
© SuperDataScienceDeep Learning A-Z STEP 1: Randomly initialise the weights to small numbers close to 0 (but not 0). STEP 2: Input the first observation of your dataset in the input layer, each feature in one input node. STEP 3: Forward-Propagation: from left to right, the neurons are activated in a way that the impact of each neuron’s activation is limited by the weights. Propagate the activations until getting the predicted result y. STEP 4: Compare the predicted result to the actual result. Measure the generated error. STEP 5: Back-Propagation: from right to left, the error is back-propagated. Update the weights according to how much they are responsible for the error. The learning rate decides by how much we update the weights. STEP 6: Repeat Steps 1 to 5 and update the weights after each observation (Reinforcement Learning). Or: Repeat Steps 1 to 5 but update the weights only after a batch of observations (Batch Learning). STEP 7: When the whole training set passed through the ANN, that makes an epoch. Redo more epochs.

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  • 2. © SuperDataScienceDeep Learning A-Z Image Source: neuralnetworksanddeeplearning.com Forward PropagationBackpropagation
  • 3. © SuperDataScienceDeep Learning A-Z Neural Networks and Deep Learning Michael Nielsen (2015) Link: http://neuralnetworksanddeeplearning.com/chap2.html Additional Reading:
  • 4. © SuperDataScienceDeep Learning A-Z STEP 1: Randomly initialise the weights to small numbers close to 0 (but not 0). STEP 2: Input the first observation of your dataset in the input layer, each feature in one input node. STEP 3: Forward-Propagation: from left to right, the neurons are activated in a way that the impact of each neuron’s activation is limited by the weights. Propagate the activations until getting the predicted result y. STEP 4: Compare the predicted result to the actual result. Measure the generated error. STEP 5: Back-Propagation: from right to left, the error is back-propagated. Update the weights according to how much they are responsible for the error. The learning rate decides by how much we update the weights. STEP 6: Repeat Steps 1 to 5 and update the weights after each observation (Reinforcement Learning). Or: Repeat Steps 1 to 5 but update the weights only after a batch of observations (Batch Learning). STEP 7: When the whole training set passed through the ANN, that makes an epoch. Redo more epochs.