2 3 5 T4 2 5 T5 1 3 5 18 2.Apriori算法 算法案例 第四次迭代：使用F3的集合，我们将创建C4。 F3 项集 支持度 {1,3,5} 2 {2,3,5} 2 C4 项集 支持度 {1,2,3,5} 1 订单编号 项目 T1 1 3 4 T2 2 3 5 T3 1 2 3 5 T4

器学习”的帖子也可能涉及“编程语言”。 此外，在处理生物医学文献时，我们也会遇到这类问题。正确地标记文献很重要，有利于研究人员对文献 进行详尽的审查。在美国国家医学图书馆（The United States National Library of Medicine），一些专业的 注释员会检查每一篇在PubMed中被索引的文章，以便将其与Mesh中的相关术语相关联（Mesh是一个大约 有28000个标签的集合）。这是一个十分耗时 page) class Inception(nn.Module): # c1--c4是每条路径的输出通道数 def __init__(self, in_channels, c1, c2, c3, c4, **kwargs): super(Inception, self).__init__(**kwargs) # 线路1，单1x1卷积层 self.p1_1 = nn.Conv2d(in_channels self.p4_1 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1) self.p4_2 = nn.Conv2d(in_channels, c4, kernel_size=1) def forward(self, x): p1 = F.relu(self.p1_1(x)) p2 = F.relu(self.p2_2(F.relu(self

Framework containers are no longer tested on Pascal GPU architectures. ‣ Transformer Engine is a library for accelerating Transformer models on NVIDIA GPUs. It includes support for 8-bit floating point Framework containers are no longer tested on Pascal GPU architectures. ‣ Transformer Engine is a library for accelerating Transformer models on NVIDIA GPUs. It includes support for 8-bit floating point Framework containers will no longer be tested on Pascal GPU architectures. ‣ Transformer Engine is a library for accelerating Transformer models on NVIDIA GPUs. It includes support for 8-bit floating point

Theano, TensorFlow, Caffe, Mxnet etc., Keras is one of the most powerful and easy to use python library, which is built on top of popular deep learning libraries like TensorFlow, Theano, etc., for creating or Cognitive Toolkit (CNTK). Theano is a python library used for fast numerical computation tasks. TensorFlow is the most famous symbolic math library used for creating neural networks and deep learning Linux or Mac)  Python version 3.5 or higher. Python Keras is python based neural network library so python must be installed on your machine. If python is properly installed on your machine, then

model and wraps the prunable blocks for sparse training using TFMOT (Tensorflow Model Optimization) library. In this case, we prune the 50% of the weights in each prunable block using magnitude-based pruning performance. Let's go ahead and strip the pruning weights from the model that were added by the TFMOT library as shown below. # Strip the pruning wrappers from the model. stripped_model = tfmot.sparsity.keras sparsity, we will use the Tensorflow Model Optimization Toolkit (TFMOT) library to cluster our model’s weights. Installing the library is fairly simple as we saw earlier. ! pip install -q tensorflow-model-optimization

up the required libraries, and loading the training and validation sets. We leverage the nlpaug library to perform the augmentations. It provides a simple 5 Maas, Andrew, et al. "Learning word vectors technologies. 2011. mechanism to chain multiple augmentations. It can be replaced with any other library per individual preference. %%capture # We will use nlpaug to augment the text samples. !pip install function. Now, let’s add some text augmentations to the mix and see if that helps. The nlpaug python library offers concise ways to apply sentence, word and character augmentations. We shuffle sentences, substitute

test_dataset.shuffle(test_dataset.cardinality()).batch(BATCH_SIZE) We will import the tensorflow_text library so that we can use the BERT model which relies on certain tensorflow ops. import os # tensorflow_text ops used in our model. import tensorflow_text as tf_text Next we will import the tensorflow_hub library so that we can import pre-trained BERT models directly from Tensorflow Hub. import tensorflow_hub

function is hθ(x) = g(θT x) = 1 1 + e−θT x = P(y = 1 | x; θ) (1) Matlab/Octave does not have a library function for the sigmoid, so you will have to define it yourself. The easiest way to do this is through

lines to code in comparison. • It is easy to debug and understand the code. • Python usage − This library is considered to be Pythonic which smoothly integrates with the Python data science stack. • It

模型服务部署 • flask $ pip install Pillow captcha pydot flask Pillow (PIL Fork) PIL(Python Imaging Library) 为 Python 解释器添加了图像处理功能。但是，在 2009 年发布 1.1.7 版本后，社区便停止更新和维护。 Pillow 是由 Alex Clark 及社区贡献者 一起开发和维护的一款分叉自