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所在平台: Udemy |
课程主页: https://www.udemy.com/course/tensorflow-interview-questions-and-answers/
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**课程名称：** TensorFlow面试题及答案 **课程概述：** 本课程由Uplatz提供，旨在帮助学员准备TensorFlow工程师职位的面试。您将学习到在TensorFlow工程师面试中最常被问到的问题。 **TensorFlow简介：** TensorFlow是Google Brain团队开发的一个强大的、面向数据流的机器学习库，于2015年开源。它易于使用，广泛应用于数值计算、神经网络及其他领域。TensorFlow是一个低级工具包，用于处理复杂的数学运算，特别适合研究人员构建和实验学习架构，并将其转化为可运行的软件。 简单来说，TensorFlow将计算表示为计算图。图中的节点代表数学运算，边代表在它们之间传递的多维数据数组（张量）。因此，TensorFlow是一个基于数据流图概念构建模型的开源深度学习库，允许创建具有多层的大规模神经网络。 **张量与TensorFlow：** 张量是深度学习中表示数据的标准。张量就是多维数组，能够表示具有更高维度的数据。深度学习处理的数据集通常具有高维度，维度指的是数据集中的不同特征。事实上，“TensorFlow”这个名字就来源于神经网络在张量上执行的操作，即张量的流动。 在TensorFlow中，“张量”指代多维数组形式的数据表示，“流动”则指对张量执行的操作序列。编写TensorFlow程序通常包含两个步骤： 1. **构建计算图** 2. **运行计算图** TensorFlow整合了机器学习和深度学习模型及算法。它使用Python作为便捷的前端，并在优化的C++环境中高效运行。TensorFlow允许开发者创建计算图进行运算。图中的每个节点代表一个数学运算，每条连接代表数据。因此，开发者无需关注如何将一个函数的输出连接到另一个函数的输入等底层细节，而是可以专注于应用程序的整体逻辑。 **薪资前景：** 根据领先的招聘网站数据，TensorFlow工程师的平均年薪为148,000美元。拥有扎实TensorFlow知识的深度学习工程师能够获得高薪，这是一个值得投身或追求的领域。
A warm welcome to the TensorFlow Interview Questions & Answers course by Uplatz.Uplatz provides this course on TensorFlow Interview Questions. You will learn the most frequently asked questions in TensorFlow engineer job interviews. As per the leading job sites, the average salary for TensorFlow jobs is $148,000. Thus Deep Learning engineers with sound knowledge of TensorFlow command premium salaries, hence it's a good area to be already in or to aspire for.What is TensorFlowTensorFlow is a powerful data flow oriented machine learning library created by the Brain Team of Google and made open source in 2015. It is designed to be easy to use and widely applicable to both numeric and neural network oriented problems as well as other domains. TensorFlow is a low-level toolkit for doing complicated math and it targets researchers who know what they're doing to build experimental learning architectures, to play around with them and to turn them into running software.Generally, it can think of as a programming system in which you represent computations as graphs. Nodes in the graph represent math operations, and the edges represent multidimensional data arrays (tensors) communicated between them. Thus TensorFlow is an open source deep learning library that is based on the concept of data flow graphs for building models. It allows you to create large-scale neural networks with many layers. Tensors and TensorFlowTensors are nothing but a de facto for representing the data in deep learning. Tensors are just multidimensional arrays, that allows you to represent data having higher dimensions. In general, Deep Learning you deal with high dimensional data sets where dimensions refer to different features present in the data set. In fact, the name "TensorFlow" has been derived from the operations which neural networks perform on tensors. It's literally a flow of tensors.In TensorFlow, the term tensor refers to the representation of data as multi-dimensional array whereas the term flow refers to the series of operations that one performs on tensors. The overall process of writing a TensorFlow program involves two steps:Building a Computational GraphRunning a Computational GraphTensorFlow bundles together Machine Learning and Deep Learning models and algorithms. It uses Python as a convenient front-end and runs it efficiently in optimized C++. TensorFlow allows developers to create a graph of computations to perform. Each node in the graph represents a mathematical operation and each connection represents data. Hence, instead of dealing with low-details like figuring out proper ways to hitch the output of one function to the input of another, the developer can focus on the overall logic of the application.
