Ultimate PyTorch Quiz

torch.random = Module for generating random numbers torch.masked = Module for performing operations on masked tensors torch.sparse = Module for handling sparse tensors torch.utils.data = Module for handling datasets and data loading

torch.utils.benchmark = Utility for benchmarking PyTorch performance torch.utils.checkpoint = Utility for checkpointing in PyTorch torch.utils.cpp_extension = Utility for integrating C++ extensions torch.utils.model_zoo = Utility for accessing pre-trained models

Named Tensors = Tensors with named dimensions Pipelined Execution = Execution strategy to improve parallelism Quantization = Technique to reduce memory usage and improve speed Distributed RPC Framework = Framework for remote procedure calls in distributed computing

<p>Autograd mechanics = Automatic differentiation for all operations on Tensors Broadcasting semantics = How PyTorch handles operations between tensors of different shapes Multiprocessing best practices = Guidelines for efficiently using multiple processes in PyTorch Gradcheck mechanics = Functionality to check the correctness of your forward and backward functions</p> Signup and view all the answers

<p>torch.nn = Module to help create and train neural networks torch.Tensor = Multi-dimensional matrix containing elements of a single data type torch::deploy = C++ utility for deploying PyTorch models torch.func = Interface for extending PyTorch with new user-defined functions</p> Signup and view all the answers

<p>Convolution Layers = Layers that apply a convolution operation to the input Pooling layers = Layers that reduce the spatial size of the input Dropout Layers = Layers that randomly set a fraction of input units to 0 at each update during training time Linear Layers = Layers that apply a linear transformation to the incoming data</p> Signup and view all the answers

<p>torch.autograd = Provides classes and functions implementing automatic differentiation of arbitrary scalar valued functions. torch.cuda = Support for CUDA tensor types that implement the same function as CPU tensors, but they utilize GPUs for computation. torch.distributed = Supports distributed computing, including various communication collectives, point-to-point communication, and more. torch.distributions = Library for probability distributions and statistical operations.</p> Signup and view all the answers

<p>Autocasting = A feature that automatically casts the inputs to the precision types that are specified in the context manager. Backward-mode Automatic Differentiation = A method to compute the gradient of any computation with respect to its inputs. Anomaly Detection = A mode that will make autograd check the computation graph during backward pass and output more understandable error messages. Profiler = A tool that collects the time and memory information about the model’s forward pass.</p> Signup and view all the answers

<p>torch.backends.cuda = Implements the same functions as CPU tensors but utilize GPUs for computation. torch.backends.mkl = Implements the same functions as CPU tensors but utilize Intel MKL for computation. torch.backends.openmp = Implements the same functions as CPU tensors but utilize OpenMP for computation. torch.backends.mps = Implements the same functions as CPU tensors but utilize NVIDIA Multi-Process Service for computation.</p> Signup and view all the answers

<p>torch.jit = A compilation stack that allows PyTorch models to be optimized for deployment and exported to other languages. torch.hub = A pre-trained model repository designed to facilitate research reproducibility and enable new research. torch.nn.init = Provides methods for initializing weights and biases. torch.optim = Implements various optimization algorithms used for training neural networks.</p> Signup and view all the answers