PyTorch is currently one of the mainstream frameworks for deep learning. It has a mature ecology, a large number of open source source codes, and the latest models. Whether in academic research or engineering, PyTorch is the mainstream choice. At the same time, PyTorch is easier to learn than other deep learning frameworks, and it is also a good choice for novices to get started.
This course organically combines algorithms, models, and basic theoretical knowledge, combined with multiple different CV and NLP practical projects, to help everyone master the basic knowledge and usage of the PyTorch framework, and learn to use the PyTorch framework to solve practical problems. Through this course, you can get started quickly and smoothly in the field of deep learning, and initially master the key skills to solve the basic problems of deep learning.
PyTorch is a Python library that is very likely to change the prospects of deep learning. I tried using PyTorch for a few weeks and was shocked by its ease of use. Among the various deep learning libraries I have used, PyTorch is the most flexible and easiest to master.
Overview of
PyTorch The founders of PyTorch said that one of their creative guidelines-they want to be a priority. This means that we can perform calculations immediately. This is exactly in line with the Python programming method, you do not need to complete all the code to run, you can easily run part of the code and check it in real time. It is very happy for me to use it as a neural network debugger.
PyTorch is a Python-based library used to provide a flexible deep learning development platform. The workflow of PyTorch is very close to Python's scientific computing library-numpy.
Now you may ask, why do we use PyTorch to build deep learning models? I can list three things that help answer:
· Easy to use API-it is as simple as Python.
Python support—As mentioned above, PyTorch can be smoothly integrated with the Python data science stack. It is very similar to numpy, and you can't even notice the difference.
·Dynamic calculation graphs—instead of predefined graphs with specific functions, PyTorch provides us with a framework so that calculation graphs can be built at runtime and even changed at runtime. This is very valuable without knowing how much memory is needed to create a neural network.
Some other advantages of PyTorch include: multi-gpu support, custom data loaders and simplified preprocessors.
Since its release in January 2016, many researchers have used it as a "go-to" library because it can easily build novel and even extremely complex graphics. Having said that, PyTorch has not been adopted by most data science practitioners for some time because it is new and in a "under construction" state.