seen from United States
seen from United States
seen from Canada
seen from Germany
seen from France
seen from United States
seen from Canada
seen from Germany
seen from China
seen from United States
seen from United States
seen from Australia
seen from Italy
seen from Germany
seen from United States
seen from Canada
seen from Malaysia
seen from Germany
seen from Netherlands
seen from United States
In this TensorFlow Interview Questions, you will learn everything about TensorFlow interview questions with detailed answers recently asked in top MNCs. If you are preparing for the TensorFlow job then this is a must-watch video for you. In this video, we have covered TensorFlow basic to advance questions so that this video will be helpful to everyone at any stage of learning TensorFlow.
Anya is live and ready to show you everything. Watch her strip, dance, and perform exclusive shows just for you. Interact in real-time and make your fantasies come true.
Free to watch • No registration required • HD streaming
Top 50 TensorFlow Interview Questions
TensorFlow is a Python-based library which is used for creating machine learning applications. It is a low-level toolkit to perform complex mathematics. It offers users the customizability option to build experimental learning architectures. It also helps the users to work with them, and to turn them into running software. It was initially created by researchers and engineers working on the Google Brain Team, and It became open source in 2015. So, anyone looking to make career in Tensorflow then you must prepare top tensorflow interview questions for increasing chance of getting select in an interview.
What do you know about Tensors?
The Tensor is a generalization of vectors or matrices of the n-dimensional array which is used in computer programming. It represents a multitude of data in the form of numbers. There are few other n-dimensional array libraries which are available on the internet such as Numpy, but TensorFlow stands apart from those libraries. It provides methods to create tensor functions and automatically compute derivatives.
A graph can conduct all operations in the Tensor. We can say that the edge of the node is known as Tensor. We need a feature vector as the initial input to implement a Tensor. The models are fed with the list of objects in the machine learning, and these objects are called feature vectors.
