MLAI/DeepLearning
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Sigmoid and SoftmaxMLAI/DeepLearning 2022. 7. 7. 18:32
1. Softmax Softmax function calculates the probability distribution of the event over k different events. This function will calculate the probabilities of each target class over all possible target classes. Equation $$P(y=j | x) = \frac{e^{x_j}}{\sum_{k=1}^K e^{x_k}}$$ Plot x = np.arange(-2.0, 6.0, 0.1) input = np.vstack([x, np.ones_like(x), 0.2 * np.ones_like(x)]) Characteristic It normalizes ..
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Activation FunctionsMLAI/DeepLearning 2020. 1. 30. 14:32
1. Overview In artificial neural networks, the activation function of a node defines the output of that node given an input or set of inputs. A standard computer chip circuit can be seen as a digital network of activation functions that can be "ON" (1) or "OFF" (0), depending on the input. This is similar to the behavior of the linear perceptron in neural networks. However, only nonlinear activa..
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Boltzmann Machine with Energy-Based Models and Restricted Boltzmann machines(RBM)MLAI/DeepLearning 2019. 10. 19. 19:36
1. Overview A Boltzmann machine (also called stochastic Hopfield network with hidden units) is a type of stochastic recurrent neural network and Markov random field. Boltzmann machines can be seen as the stochastic, generative counterpart of Hopfield networks. They were one of the first neural networks capable of learning internal representations, and are able to represent and (given sufficient ..
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Classify Deep LearningMLAI/DeepLearning 2019. 10. 16. 22:01
1. Overview Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and the desired output value (also called the supervisory..
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Softmax and Cross-Entropy with CNNMLAI/DeepLearning 2019. 10. 16. 15:56
1. Overview 2. Description How come two output values add up to one? 2.1 Softmax function(Normalized exponential function) $$f_{j}(z)=\frac{e^{zj}}{\sum_{k}e^{zk}}$$ Normally, the dog and the cat neurons would have any kind of real values. Applying the softmax function which is written up over there at the top, and that would bring these values to be between zero and one and it would make them a..
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Artificial neural network(ANN)MLAI/DeepLearning 2019. 10. 5. 13:39
1. Overview Artificial neural networks (ANN) or connectionist systems are computing systems that are inspired by, but not identical to, biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with task-specific rules. For example, in image recognition, they might learn to identify images that cont..
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Convolutional Neural Networks(CNN)MLAI/DeepLearning 2019. 9. 30. 17:28
1. Overview In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery. CNNs are regularized versions of multilayer perceptrons. Multilayer perceptrons usually mean fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. The "fully-connectedness" of the..
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Difference between Deep Learning and Shallow learningMLAI/DeepLearning 2019. 9. 25. 07:27
1. Overview Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on artificial neural networks. Learning can be supervised, semi-supervised or unsupervised. Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been ap..