The current buzz in data science and big data is around the promise of deep learning, especially when working with unstructured data. One of the most popular frameworks for building deep learning applications is PyTorch, in large part because of their focus on ease of use. In this episode Adam Paszke explains how he started the project, how it compares to other frameworks in the space such as Tensorflow and CNTK, and how it has evolved to support deploying models into production and on mobile devices.
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- Your host as usual is Tobias Macey and today I’m interviewing Adam Paszke about PyTorch, an open source deep learning platform that provides a seamless path from research prototyping to production deployment
- How did you get introduced to Python?
- Can you start by explaining what deep learning is and how it relates to machine learning and artificial intelligence?
- Can you explain what PyTorch is and your motivation for creating it?
- Why was it important for PyTorch to be open source?
- There is currently a large and growing ecosystem of deep learning tools built for Python. Can you describe the current landscape and how PyTorch fits in relation to projects such as Tensorflow and CNTK?
- What are some of the ways that PyTorch is different from Tensorflow and CNTK, and what are the areas where these frameworks are converging?
- How much knowledge of machine learning, artificial intelligence, or neural network topologies are necessary to make use of PyTorch?
- What are some of the foundational topics that are most useful to know when getting started with PyTorch?
- Can you describe how PyTorch is architected/implemented and how it has evolved since you first began working on it?
- You recently reached the 1.0 milestone. Can you talk about the journey to that point and the goals that you set for the release?
- What are some of the other components of the Python ecosystem that are most commonly incorporated into projects based on PyTorch?
- What are some of the most novel, interesting, or unexpected uses of PyTorch that you have seen?
- What are some cases where PyTorch is the wrong choice for a problem?
- What is the process for incorporating these new techniques and discoveries into the PyTorch framework?
- What are the areas of active research that you are most excited about?
- What are some of the most interesting/useful/unexpected/challenging lessons that you have learned in the process of building and maintaining PyTorch?
- What do you have planned for the future of PyTorch?
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- University of Warsaw
- Polish Olympiad In Informatics
- Deep Learning
- Automatic Differentiation
- Torch 7
- Tensorflow 2
- EPFL (Ecole polytechnique fédérale de Lausanne)
- Transfer Learning
- Reinforcement Learning