hyperparameter values which achieve the minimum loss are the winners. Let's start by importing the relevant libraries and creating a random classification dataset with 20 samples, each one assigned to one of the five architectures for scalable image recognition. The output of a cell is the concatenated output of all the component blocks. The inputs to the cell are the outputs of the last two cells. The blocks are predicted

by various libraries such as 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 for creating deep learning models. Overview of Keras Keras runs on top of open source machine libraries like TensorFlow, Theano or Cognitive Toolkit (CNTK). Theano is a python library used for fast numerical framework developed by Microsoft. It uses libraries such as Python, C#, C++ or standalone machine learning toolkits. Theano and TensorFlow are very powerful libraries but difficult to understand for creating

computational framework with a Python front end. Functionality can be easily extended with common Python libraries such as NumPy, SciPy, and Cython. Automatic differentiation is done with a tape-based system at following CVEs might be flagged but were patched by backporting the fixes into the corresponding libraries in our release: PyTorch Release 23.07 PyTorch RN-08516-001_v23.07 | 12 ‣ CVE-2022-45198 - following CVEs might be flaggted but were patched by backporting the fixes into the corresponding libraries in our release: ‣ CVE-2022-45198 - Pillow before 9.2.0 performs Improper Handling of Highly Compressed

EasyVision EasyRec GraphLearn EasyTransfer 标准化: Standard Libraries and Solutions 标准化: Standard Libraries EasyRec: 推荐算法库 标准化: Standard Libraries ImageInput Data Aug VideoInput Resnet RPNHead Classification 性能优越:  分布式存储  分布式查询 功能完备:  GSL/负采样  主流图算法  异构图 (user/item/attribute)  动态图 标准化: Standard Libraries Graph-Learn: 分布式图算法库 标准化: Standard Solutions Continuous Optimization: Active learning Data

文本方面的词袋模型、词嵌入模型等 3. 特征提取 18 许永洪,吴林颖.中国各地区人口特征和房价波动的动态关系[J].统计研究,2019,36(01) 1.PCA(Principal Component Analysis，主成分分析) PCA 是降维最经典的方法，它旨在是找到数据中的主成分，并利 用这些主成分来表征原始数据，从而达到降维的目的。 PCA 的思想是通过坐标轴转换，寻找数据分布的最优子空间。 的样本降低到? 维 步骤 3. 特征提取 降维 19 许永洪,吴林颖.中国各地区人口特征和房价波动的动态关系[J].统计研究,2019,36(01) 2. ICA(Independent Component Analysis，独立成分分析) ICA独立成分分析，获得的是相互独立的属性。ICA算法本质寻找一 个线性变换 ? = ??，使得 ? 的各个特征分量之间的独立性最大。 PCA 对数据

30 3.PCA(主成分分析) 01 降维概述 02 SVD(奇异值分解) 03 PCA(主成分分析) 31 3.PCA(主成分分析) 主成分分析（Principal Component Analysis,PCA）是一种降维方法， 通过将一个大的特征集转换成一个较小的特征集，这个特征集仍然包含 了原始数据中的大部分信息，从而降低了原始数据的维数。 减少一个数据集的特征数 Dimensionality of Data with Neural Networks.[J]. Science, 2006. [3] Jolliffe I T . Principal Component Analysis[J]. Journal of Marketing Research, 2002, 87(4):513. [4] 李航. 统计学习方法[M]. 北京: 清华大学出版社,2019

models. For example, tensorflow has a tight integration with Tensorflow Lite (TFLite) and related libraries, which allow exporting and running models on mobile devices. Similarly, TFLite Micro helps in running models, by allowing export of models with 8-bit unsigned int weights, and having integration with libraries like GEMMLOWP and XNNPACK for fast inference. Similarly, PyTorch uses QNNPACK to support quantized

of fully connected layers. Exercise: Sparsity improves compression Let's import the required libraries to start with. We will use the gzip python module for demonstrating compression. The code for this case of this convolutional layer, we can drop rows, columns, kernels, and even whole channels. Libraries like XNNPACK3,4 can help accelerate networks on a variety of web, mobile, and embedded devices,

4-15. The encoder RNN transforms the english sequence to a latent representation . The decoder component receives and outputs spanish language sequence . Figure 4-15: RNN Encoder-Decoder This basic idea are grouped under the Sparse group. After input sequence and the attention parameters, the next component to attack is the softmax computation. The Low Rank methods project the keys and the values to a

Linear Regression September 13, 2023 16 / 31 GD Algorithm (Contd.) In more details, we update each component of θ according to the fol- lowing rule θj ← θj − α∂J(θ) ∂θj , ∀j = 0, 1, · · · , n Calculating