PyTorch is an emerging machine learning framework developed by Facebook’s AI research team. Due to its flexibility, dynamic network model and other characteristics, it has developed rapidly and is currently a Top 2 machine learning framework. Let’s take a look at the inner workings of PyTorch with a PPT.

Christian Perone is a senior machine learning/data science researcher and software engineer. Worked at HEWLETT-PACKARD and now teaches at the Montreal Institute of Engineering, one of Canada’s top engineering schools,


Torch is a modular open source library for machine learning and scientific computing, originally developed by NYU researchers for academic research.


The library improves performance by leveraging the LuaJIT compiler, and the C-based NVIDIA CUDA extensions enable the Torch to take advantage of GPU acceleration.


Many developers use Torch as a GPU-enabled alternative to NumPy; Other developers use it to develop deep learning algorithms.


The Torch became famous because of its use on Facebook and Twitter. PyTorch, as its name implies, uses Python as the development language.


PyTorch is a relatively new deep learning framework that focuses on dynamic network models. PyTorch provides a lower-level approach compared to its peers and is more flexible for users with a more mathematical background.


Despite its short life, PyTorch has gained momentum and has become a Top 2 mainstream framework for machine learning (the shortest purple curve in the bottom right corner of the image below).





Here, we take a closer look at PyTorch’s content mechanism through Perone’s PPT.



























































































PPT download:


The complete PPT document has been packaged and is easy to obtain:

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