Deep Learning with PyTorch
Previously, you may have come across open-source libraries offering reinforcement learning environments.
The field of RL has made exciting advancements, especially when it is combined with deep learning. The new developments allow us to solve even more challenging problems. Several deep learning techniques and resources have contributed to this progress. With PyTorch, you can create sophisticated deep-learning models with just a few lines of Python code.
There is no comprehensive guide to PyTorch deep learning in this article. It is a vast and constantly evolving field. Nevertheless, we’ll explore PyTorch’s inner workings, including nitty-gritty implementation details, along with higher-level libraries that use PyTorch to attack common deep-learning problems. We will also demonstrate PyTorch ignite through various example presentations.
Tensors
As their foundation, DL toolkits typically use tensors. Multidimensional arrays, such as tensors, consist of multiple dimensions and are also termed multi-dimensional arrays. Mathematically, a solitary number can be compared to a zero-dimensional point, whereas a vector is like a one-dimensional line segment, and a matrix is two-dimensional. Despite the lack of a specific name, parallelepiped shapes are often used to represent numbers in three dimensions. We can continue to refer to collections beyond two dimensions as tensors.
A tensor in deep learning differs from a tensor in tensor calculus or tensor algebra for a number of reasons. As opposed to deep learning tensors, which are essentially multidimensional arrays, mathematical tensors are mappings between vector spaces. It’s important to exercise caution when using different terms for the same concept among mathematicians.
The creation of tensors
NumPy is a multidimensional array manipulation library, so understanding the library is crucial. A NumPy tensor is an array in NumPy. Various computational applications use tensors to store scientific data. An image in color, for example, can be represented as a 3D tensor comprised of width, height, and color plane dimensions.
This article does not have a notebook to download. I have added all of the code snippet in the article itself.