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PyTorch is developed by Facebook's AI Research lab & is known as an open-source machine learning library based on the Torch library. It is mostly used for applications like computer vision and natural language processing. PyTorch library can be used with both languages - Python as well as C++. PyTorch uses very core Python concepts such as classes, structures, and conditional loops that are a lot familiar and more intuitive to understand. It mainly uses the basic and familiar programming paradigms rather than inventing its own.
PyTorch skills test helps tech recruiters and hiring managers assess candidate's PyTorch skills. PyTorch online test is designed by experienced subject matter experts (SMEs) to evaluate and hire PyTorch expert as per the industry standards.
PyTorch skills testhelps to screen the candidates who possess traits as follows:
Good knowledge of various computations in PyTorch library
Experience with PyTorch model methods and PyTorch tensor
Understanding of new network models, GPU devices, and optimizers
Familiarity with terms like functions, objects, and classes
PyTorch skills test is a secure and reliable way of candidate assessment. You can use our role-based access control feature to restrict system access based on the roles of individual users within the recruiting team. Features like window violation, image, audio & video proctoring help detect cheating during the test.
This test may contain MCQs (Multiple Choice Questions), MAQs (Multiple Answer Questions), Fill in the Blanks, Whiteboard Questions, Audio / Video Questions, LogicBox (AI-based Pseudo-Coding Platform), Coding Simulators, True or False Questions, etc.
This PyTorch Skills Test is useful for hiring
Machine Learning Engineer
You are training a binarized neural network in PyTorch. Clamping of neuron outputs is an extremely important step in training such networks. Which of the following functions lets you clamp a given tensor in the range [-1, 1]?
The parameters of a neural network model are optimized using optimizers like Stochastic Gradient Descent, Adam, etc. The Optim Module in PyTorch has various such optimizers implemented in it. Consider one such optimizer:
optimizer = torch.optim.SGD(X, lr = 0.01, momentum=0.9)
What should be the value of X in the above optimizer declaration?