Everything You Need to Know About AIMSICD

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Understanding what is Artificial Intelligence and how does Machine Learning and Deep Learning powers it is an overwhelming experience. We are a group of self-taught engineers who have gone through that experience and we are sharing in blogs our understanding and what helped us in simplified form, so that anyone who is new to this field can easily start making sense of the technicalities of this technology. It gets certain number of inputs and a bias value. When a signal value arrives, it gets multiplied by a weight value. If a neuron has 4 inputs, it has 4 weight values which can be adjusted during training time. A connection always has a weight value associated with it. Goal of the training is to update this weight value to decrease the loss error. It squashes the values in a smaller range viz. In this article I have explained different activation functions available. It takes input signals values and passes them on to the next layer. In our network we have 4 input signals x1, x2, x3, x4. One hidden layer is a collection of neurons stacked vertically Representation. In our image given below we have 5 hidden layers. In our network, first hidden layer has 4 neurons nodes , 2nd has 5 neurons, 3rd has 6 neurons, 4th has 4 and 5th has 3 neurons. Last hidden layer passes on values to the output layer. All the neurons in a hidden layer are connected to each and every neuron in the next layer, hence we have a fully connected hidden layers. With this layer we can get desired number of values and in a desired range. In this network we have 3 neurons in the output layer and it outputs y1, y2, y3. Desired input shape for our network is 1, 4, 1 if we feed it one sample at a time. If we feed samples input shape will be , 4, 1. Different libraries expect shapes in different formats. If the weight from node 1 to node 2 has greater magnitude, it means that neuron 1 has greater influence over neuron 2. A weight brings down the importance of the input value. Weights near zero means changing this input will not change the output. Negative weights mean increasing this input will decrease the output. A weight decides how much influence the input will have on the output. Sometimes we refer forward propagation as inference. Second layer takes values from first layer and applies multiplication, addition and activation operations and passes this value to the next layer. Same process repeats for subsequent layers and finally we get an output value from the last layer. To calculate error we compare the predicted value with the actual output value. We use a loss function mentioned below to calculate the error value. Then we calculate the derivative of the error value with respect to each and every weight in the neural network. Back-Propagation uses chain rule of Differential Calculus. In chain rule first we calculate the derivatives of error value with respect to the weight values of the last layer. We call these derivatives, gradients and use these gradient values to calculate the gradients of the second last layer. We repeat this process until we get gradients for each and every weight in our neural network. Then we subtract this gradient value from the weight value to reduce the error value. In this way we move closer descent to the Local Minima means minimum loss. At each iteration we use back-propagation to calculate the derivative of the loss function with respect to each weight and subtract it from that weight. Learning rate determines how quickly or how slowly you want to update your weight parameter values. It is the repeatability or reproducibility of the measurement. In regularization we penalise our loss term by adding a L1 LASSO or an L 2 Ridge norm on the weight vector w it is the vector of the learned parameters in the given algorithm. Normalization is a good technique to use when you do not know the distribution of your data or when you know the distribution is not Gaussian a bell curve. It is good to speed up the learning process. The cost function is the average of the loss functions of the entire training set. Accuracy, loss, validation accuracy, validation loss, mean absolute error, precision, recall and f1 score are some performance metrics. Sign in Get started. Stochastic Gradient Descent, with support for momentum. Adaptive learning rate optimization method proposed by Geoff Hinton. Adaptive Moment Estimation Adam that also uses adaptive learning rates. Never miss a story from Hacker Noon , when you sign up for Medium. Get updates Get updates.

Everything You Need to Know About AIMSICD

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Перевод 'everything you need to know' на русский

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Everything You Need to Know About Messages in iOS 10

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