递归神经网络(Recurrent Neural Networks, RNNs):处理序列数据和时间序列,广泛应用于自然语言处理和语音识别。 · 1.2 迁移学习(Transfer Learning) Transfer Learning 迁移学习允许模型在一个任务上获得的知识迁移到另一个相关任务上,提高了学习效率和模型的泛化能力。 ·预训练模型(Pre-trained Models):使用在大规模数...
Deep neural networks (DNNs) excel at visual recognition tasks and are increasingly used as a modeling framework for neural computations in the primate brain. Just like individual brains, each DNN has a unique connectivity and representational profile. He
[2] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.pdf tensorflow 源码https://github.com/tensorflow/models/blob/master/research/slim/nets/alexnet.py caffehttps://git...
wide部分是一个GLM,结构如Figure1左侧wide models所示。 y={\bf w}^{T}{\bf x}+b, \\ 特征x:原始输入特征、交叉变换特征(增加非线性) \phi_k(x)=\prod_{i=1}^{d}x_i^{c_{ki}}\quad\quad c_{ki} \in\{0,1\} \\ 其中,k表示第k个组合特征。i表示输入X的第i维特征。当且仅当 xi是...
Therefore, the prediction task has been formulated as a classification problem combined with feature representations, and related work tried to solve the problem by utilizing machine learning approaches such as random forests1,2, neural networks3, sparse linear models4,5 and support vector machines6....
深度神经网络(Deep Neural Networks,DNN)可以理解为有很多隐藏层的神经网络,又被称为深度前馈网络(DFN),多层感知机(Multi-Layer perceptron,MLP),其具有多层的网络结构,如下图所示: 本篇博客的主要内容就是利用tf.keras来搭建一个具有20层隐藏层的网络结构,去实现一个简单的分类问题,分类的数据与TensorFlow2.0(二)...
Bengio, Y., Yao, L., Alain, G., Vincent, P.: Generalized denoising auto-encoders as generative models. In: NIPS. pp. 899–907 (2013) PNN 论文:Product-based Neural Networks for User Response Prediction 地址:arxiv.org/pdf/1611.0014 论文认为FNN中embeddings初始化的质量存在很大局限,大大取决于...
·深度神经网络(Deep Neural Networks):由多层神经元构成的网络,用于处理复杂的数据模式。例如,卷积神经网络(CNN)和循环神经网络(RNN)。 ·自监督学习(Self-Supervised Learning):通过自动生成标签进行训练,提高数据利用效率。例如,BERT和GPT的训练方法。 ·迁移学习(Transfer Learning):将预训练的模型应用于不同的任务...
MobileNet v1:2017,MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications MobileNet v2:2018,Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation TensorFlow实现: https://github.com/tensorflow/models/blob/master/research/slim/nets/mo...
我们集中于 deep neural network models, 但是我们的模型也可以通过微小的改变就很容易处理不同的结构,以及不同的问题设定,如分类,回归,策略梯度强化学习。在 meta-learning 中,所训练的模型的目标是:快速的从少量新的数据上学习一个新的任务,然后该模型通过 meta-learner 来训练,使得能够在大量不同的任务上进行...