TensorFlow Interview Questions & Answers
Created by Uplatz Training | Last updated 4/2021
Duration: 3h 7m | 9 sections | 9 lectures | Video: 1280x720, 44 KHz | 1.3 GB
Genre: eLearning | Language: English
Go through the top questions (with answers) asked in TensorFlow job interviews. Become a top Deep Learning / ML Engineer
What you'll learn
TensorFlow interview questions with answers
Crack TensorFlow and Deep Learning / Machine Learning job interviews
Enhance your knowledge of TensorFlow
Become a TensorFlow / Deep Learning Engineer
Get aware about the most trending topics on TensorFlow
Requirements
Enthusiasm and determination to make your mark on the world!
Description
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 isTensorFlow
TensorFlow 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 TensorFlow
Tensors 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 Graph
Running a Computational Graph
TensorFlow 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.
Who this course is for:Data Scientists & Artificial Intelligence ArchitectsMachine Learning and Deep Learning EngineersCandidates preparing for TensorFlow and ML/DL interviewsSoftware Developers, Data EngineersTensorFlow Application DevelopersResearchers and ML EnthusiastsStudents aspiring to become Deep Learning EngineersResearch Scientists (Deep Learning)
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