Role of Pytorch¶
Welcome to Role of PyTorch in Deep Learning! In this part, you will be get yourself acquainted with Deep Learning and PyTorch.
Deep Learning Unleashed¶
To understand the impact of deep learning, let's look at how things used to work. Before deep learning, machine learning relied on something called feature engineering. This meant manually creating ways to transform raw data into a format that made it easier for algorithms to understand and make accurate predictions.
For instance, if we wanted to teach a computer to recognize handwritten digits like ones and zeros, we had to design specific filters to detect the edges in the images. We would also count features like the number of enclosed areas (holes) in the digits, which helps to distinguish between a zero and an eight, or a loopy two.
Feature engineering required a lot of manual effort to figure out the best transformations for the data. Deep learning changed this by allowing algorithms to learn these transformations automatically from the raw data, making the process much more efficient and effective.
PyTorch for Deep Learning¶
PyTorch is a Python library designed to simplify the creation of deep learning projects. It's known for its flexibility, allowing users to build models using familiar Python syntax. This ease of use initially made it popular among researchers, and over the years, it has become one of the leading tools for deep learning across various applications.
Just as Python is great for general programming, PyTorch is an excellent entry point for deep learning. It is not only user-friendly but also robust enough for professional, high-stakes projects. PyTorch stands out because of its straightforward syntax, efficient API, and easy debugging, making it ideal for beginners. While we strongly recommend starting with PyTorch, whether it will be the only deep learning library you use is up to you.
At its foundation, deep learning involves complex mathematical functions that map inputs to outputs. PyTorch simplifies this with its core data structure, the tensor, which is a multidimensional array similar to those in NumPy. Additionally, PyTorch supports fast mathematical operations on specialized hardware, making it efficient to create and train neural networks on both single machines and large-scale computing environments.