11/21/2023 0 Comments Ian goodfellow deep learning pdf![]() Introduces the reader to a number of classical approximation theorems of analytic flavor. Part II The main idea of this part is that neural networks are universal approximators, that is, the output of a neural network can approximate a large number of types of targets, such as continuous function, square integrable, or integrable functions, as well as measurable functions. ![]() The architecture of a network as well as the backpropagation method used for training the networkis explained in detail. The study of networks of neurons is done in Chap.6. Also, some applications tologistic regression and classification are included. Introduces the concept of abstract neuron and presents a few classical types of neurons, such as: the perceptron, sigmoid neuron, linearneuron, and the neuron with a continuum input. Otheralgorithms contained in the chapter are the linear search method, momentummethod, simulated annealing, AdaGrad, Adam, AdaMax, Hessian, andNewton’s methods. The most used isthe Gradient Descent Algorithm, which is presented in full detail. They are needed for the minimization of the associated cost function. Presents a series of classical minimization algorithms. Some of these cost functions aresuited for learning random variables, while others for learning deterministic functions. They include the following: the supremum error function,L2-errorfunction, mean square error function, cross-entropy, Kullback-Leibler divergence, Hellinger distance, and others. This function is alsoknown under the names of cost function, error function, or loss function.ĭescribes some of the most familiar cost functions used in neuralnetworks. They are classified into three main classes: sigmoid type (logistic,hyperbolic tangent, softsign, arctangent), hockey-stick type (ReLU, PReLU,ELU, SELU), and bumped type (Gaussian, double exponential).During the learning process a neural network has to adjust parameterssuch that a certain objective function gets minimized. The choice of these activation functions defines different types of networks.Ĭontains a systematic presentation of the zoo of activation functions that can be found in the literature. Asan example, we provide the relation between linear regression and neural networks.In order to learn a nonlinear target function, a neural network needs to use activation functions that are nonlinear. More neural units can work together as a neural network. The optimization of the process involves the minimization of a cost function, such as volume, energy,potential, etc. These topics introduce the reader to the process of adjusting a rate, a flow, or a current that feeds a tank, cell, fund, transistor,etc., which triggers a certain activation function. Introduces a few daily life problems, which lead toward the concept of abstract neuron. Metacademy is a great resource which compiles lesson plans on popular machine learning topics.įor Beginner questions please try /r/LearnMachineLearning, /r/MLQuestions or įor career related questions, visit /r/cscareerquestions/ Please have a look at our FAQ and Link-Collection Rules For Posts + Research + Discussion + Project + News on Twitter Chat with us on Slack Beginners:
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