neural networks for machine learning quiz answers

\):The chain rules are updated as follows:\[\begin{split}\frac{c_0}{\partial \omega_{jk}^{(L)}}= \frac{\partial z_j^{(L)}}{\partial \omega_{jk}^{(l)}}\ Frac{\partial a_j^{(L)}}{\partial z_j^{(l)}}\frac{\partial c_0}{\partial a_j^{(L)}}\=a^{l-1}_k \sigma\prime (z^ {(l)}_j) 2 (a^{(l)}_j-y_j) \end{split}\]And to push this formula to other layers ( \frac{c}{\partial \omega_{jk}^{(L)}}\) , only the \ (\frac{\partial c}{\partial a_j^{) in the formula is required ( L)}}\) .Summarized as follows:Therefo

5.1 Cost FunctionSuppose the training sample is: {(x1), Y (1)), (x (2), Y (2)),... (x (m), Y (m))}L = Total No.of layers in NetworkSl= no,of units (not counting bias unit) in layer LK = number of output units/classesThe neural network, L = 4,S1 = 3,s2 = 5,S3 = 5, S4 = 4Cost function for logistic regression:The cost function of a neural network:　　　5.2 Reverse Propagation Algorithm backpropagationA popular ex

training:Eventually:Look at the weights for each unit, sort of like a number template.Why the simple learning algorithm is insufficienta The layer network with a winner in the top layer are equivalent to have a rigid template for each shape., Haven Winner is the template, which has the biggest overlap with the ink.the ways in which hand-written digits vary is much too complicated to being captured by simple template matches of whole s Hapes.–to captu

Discovery modeThe linear model and the neural network principle and the goal are basically consistent, the difference manifests in the derivation link. If you are familiar with the linear model, the neural network will be well understood, the model is actually a function from input to output, we want to use these models to find patterns in the data, to discover the existence of the function dependencies, of

Blog has migrated to Marcovaldo's blog (http://marcovaldong.github.io/) The tenth lecture of Professor Geoffery Hinton, neuron Networks for machine learning, describes how to combine the model and further introduces the complete Bayesian approach from a practical point of view. Why it helps to combine models In this section, we discuss why you should combine many

+ b.tC. C = a.t + bD. C = a.t + b.t9. Please consider the following code: C results? (If you are unsure, run this lookup in Python at any time). AA = Np.random.randn (3, 3= NP.RANDOM.RANDN (3, 1= a*bA. This will trigger the broadcast mechanism, so B is copied three times, becomes (3,3), * represents the matrix corresponding element multiplied, so the size of C will be (3, 3)B. This will trigger the broadcast mechanism, so B is duplicated three times, becomes (3, 3), * represents matrix multipli

http://blog.csdn.net/pipisorry/article/details/4397356Machine learning machines Learning-andrew NG Courses Study notesNeural Networks Representation Neural network representationnon-linear Hypotheses Nonlinear hypothesisNeurons and the brain neurons and brainsModel representation models representExamples and intuitions

learning.• It is hard-to-say what's the aim of unsupervised learning is.–one Major aim is to create a internal representation of the input that's useful for subsequent supervised or reinforce ment Learning.–you can compute the distance to a surface by using the disparity between the images. But your don ' t want to learn to compute disparities by stubbing your t

This article mainly introduces the knowledge of Perceptron, uses the theory + code practice Way, and carries out the learning of perceptual device. This paper first introduces the Perceptron model, then introduces the Perceptron learning rules (Perceptron learning algorithm), finally through the Python code to achieve a single layer perceptron, so that readers a

weight vector and the input vector are not more than 90 degrees, so their point set is positive, so the correct result can be obtained. Conversely, if we have a weighted value such as red, on the wrong side, with an input angle of more than 90 degrees,The weighted value and the input point set are negative, less than 0, so the perceptron will say no, or 0, in this case the wrong answer.Another example, the correct result is 0.In this example, any weight vector with input less than 90 degrees ge

equivalent ways to write the equations for a binary threshold neuron:Rectified Linear neurons(sometimes called linear threshold neurons)They compute a linear weighted sum of their inputs.The output is a non-linear function of the total inputSigmoid neurons this neuron is often usedThese give a real-valued output is a smooth and bounded function of the their total input.–typically They use the logistic function–they has nice smooth derivatives, the derivatives change continuously and they ' re n

numerals. For example: The first neuron (representing 0) output value = 1, the other 2. What is the hidden layer doing? One possible explanation: Assume that the 1th neuron of the hidden layer is only used to detect the presence of the following image:each neuron in the hidden layer learns different parts:Decision:toward deep learning (toward Deepin learning)1. Deep Ne