"PyTorch is newer, there's less existing code, it's still subject to change."
"PyTorch can do most of the work for us; we only have to design our model and know which loss and optimizer we should use."
"One thing I really like about PyTorch is that it's highly expressive."
"If you type in pytorch a times B plus c uh what this is going to do is it's going to first multiply Adam B A and B and store that result into memory and then it is going to add C by reading that result from memory."
"This is how you can actually do it inside PyTorch."
"PyTorch is our open-source framework for pre-training and fine-tuning."
"PyTorch is basically very similar to TensorFlow in that it's a deep learning library and it offers like neural network building blocks, auto-differentiation, GPU training, and a lot more."
"PyTorch is definitely preferred in the research and startup ecosystem."
"Keras has dominated on Kaggle for a long time and now PyTorch is..., I'd say maybe Keras and Pytorch, at least last time I checked, was sort of roughly equivalent, the trajectory on PyTorch was very strong."
"Thank you for your time and attention, hope to see you around the PyTorch forums."
"Unparalleled flexibility in model design with PyTorch's automatic differentiation engine."
"Welcome to my video series on PyTorch."
"I'm really going to try to focus especially on the fundamentals of PyTorch."
"The torch.tensor object is the meat and potatoes of everything you're going to be doing in PyTorch."
"PyTorch essentially is a Python package that allows you, among many things, to train deep learning models in Python."
"In order to use PyTorch well for machine learning, you have to have a fantastic understanding of the basic building blocks of PyTorch, and that's the torch.tensor."
"Because PyTorch has GPU accelerated operations, the software is able to take integrals in such a way that GPU is used and speeds up the computations massively."
"What PyTorch starts to really differ from NumPy is in terms of automatically computing the gradients of operations."
"Pytorch... we don't have to keep track of what our coefficients, or parameters, or weights are - Pytorch does that for us."
"PyTorch is a Python package that provides two high-level features: tensor computation with strong GPU acceleration and deep neural networks built on a tape-based auto-grad system."
"If you're a beginner, a good way to start with PyTorch is to read the official tutorials."
"It's built on top of PyTorch and provides a unified API interface to perform various tasks."
"For 3D deep learning tasks, there weren't any good tools which fulfill all the requirements... so we built PyTorch 3D."
"It's so critical to have open source software frameworks like PyTorch to really enable us to realize the algorithms."
"The autograd feature of PyTorch is a large part of what makes PyTorch a fast and flexible framework for building deep learning projects."
"I hope this video gave you a better intuition for how hooks work in PyTorch."
"All the assignments are going to require training neural networks in PyTorch."
"The beauty of PyTorch... is that you don't need to worry about computing the gradient."
"With GPT-4, you can say, 'I want to solve this problem; what's a good starting architecture? Create the code for me in PyTorch.'"
"Before we get started, I want to make sure you have a basic understanding of deep learning and PyTorch syntax."
"This model shows the common structure of a PyTorch model."
"The first concept of PyTorch is this dataset class and is the way that Python has to deal with datasets."
"I am your host Charles Frye, and I will be telling you today about how to instrument weights and biases with PyTorch."
"PyTorch really lets you get involved with that whole feed forward back propagation neural network."
"The sequential class allows us to build neural networks on the fly without having to define an explicit class."
"When we use the sequential class to build a PyTorch network, we construct the forward method implicitly by sequentially building our network's architecture."
"A PyTorch sequential module is a container class that allows us to compose neural network modules."
"The cool thing is that PyTorch has wrapped those particular functions and operations inside of a neural network module itself."
"You can think about tensors as the equivalent in PyTorch to NumPy arrays; they're essentially multi-dimensional arrays that you can manipulate in different ways."
"One of the really cool things with PyTorch is autograd."
"By the time you come to the end of it, you'll feel fairly familiar and comfortable with PyTorch."
"Building PyTorch models is just programming in Python."
"The core principle we drive forward in PyTorch is that building models leverages a familiar imperative and object-oriented style."
"Torch script and the PyTorch JIT comprise a compiler infrastructure that enables the gradual and fast transition of PyTorch code from research to production."
"Torch Script is a statically typed subset of Python for representing PyTorch models."
"The PyTorch just-in-time compiler performs runtime optimizations to improve the performance of your model."
"Torch Serve is the PyTorch model serving solution that covers all these needs and more."
"Pi torch is an open-source deep learning framework that we're going to be using for the ML agents."