Learning Structured Sparsity in Deep Neural Networks 1. 文章介绍 DNN,尤其是CNN,已经通过从大量数据中的大规模学习在计算机视觉领域取得了巨大的成功,但是这样的大模型部署是有问题的。为了减少计算成本,许多研究方法被用来压缩DNN的大小,包括稀疏正则化、连接剪枝和低秩近似,稀疏正则化和连接剪枝通常会得到一个非...
Batch normalization This technique normalizes the inputs of each layer, aiming to improve the stability, performance, and speed of the neural network. It also helps in reducing the sensitivity to the initial starting weights. Dropout Another regularization method, dropout randomly ignores a set ...
DenseNet was designed to connect layers in convolutional neural networks with the following motivations: to alleviate the vanishing gradient problem (it is hard to backprop loss signals through a very deep network), to bolster feature propagation, encourage the network to reuse features, and reduce ...
An interesting technique that is frequently used in dynamical supervised learning tasks is to replace the actual output y(t) of a unit by the teacher signal d(t) in subsequent computation of the behavior of the network, whenever such a value exists. We call this technique teacher forcing. —...
What is overfitting (deep learning)? 机器学习技巧6: 加速神经网络训练 (深度学习)? Speed up neural network training (deep learning)? 机器学习技巧7: 处理不均衡数据 (深度学习)! Dealing with imbalanced data (deep learning) 机器学习技巧8: 为什么要 Batch Normalization 批标准化 (深度学习) 机器学习...
Here, we construct a Convolutional Neural Network (CNN) model using Keras Core. It starts by defining an input layer that accepts images of shape(32, 32, 3). Then, it creates three blocks of layers, each consisting of two convolutional layers followed by batch normalization and dropout. The...
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2. Batch Normalization It is a specific math function with two parameters: expectation and variance. Its role is to normalize the values and equalize them in a particular range convenient for activation function. Remember that the normalization is carried out before the activation function. ...
The CGAN is a type of generative adversarial network (GAN), which is now a well-known structure in the field of machine learning, more specifically, deep learning. The concept behind the GAN is like a game between two adversarial neural networks or players. Player one is called the "generat...
Leverage domain expertise to identify models that are commonly used and suitable for similar tasks in the domain. An important part of this is considering different types of machine learning models such as linear models, tree-based models, support vector machines (SVMs), neural networks, ensemble ...