Since it’s arrival in January 2017, PyTorch has captured the minds of Machine Learning researchers and developers. Now two years later, PyTorch has matured into production-ready Machine Learning framework with ever-growing examples, use cases and applications supported by a robust community. Similarly, Machine Learning and it’s subdiscipline Deep Learning has gained immense popularity closely linked to the availability of libraries such as PyTorch and promising results across domains such as Computer Vision and Natural Language Understanding.
In this tutorial, you will learn PyTorch via several examples. As we learn PyTorch, we will implement use cases such as image classification, text classification, and regression modeling. We will do hands-on exploration with transfer learning, working with training and test datasets and learn about taking PyTorch models to production.
Familiarity with Python language and knowledge of machine learning terms and concepts.
Materials or downloads needed in advance:
Notebooks for PyTorch labs will be available a week prior to the tutorial session. At that time (1 week prior to tutorial), we will provide an update on the availability of a cloud-based lab environment or instructions to prepare your own docker based lab environment.
What you’ll learn:
- PyTorch concepts
- How to build machine learning models in PyTorch
- Image Classification, Text Classification and Linear modeling
- Transfer learning
- Working with datasets in PyTorch for machine learning
Mo Patel is an independent deep learning consultant advising individuals, startups, and enterprise clients on strategic and technical AI topics. Mo has successfully managed and executed data science projects with clients across several industries, including cable, auto manufacturing, medical device manufacturing, technology, and car insurance. Previously, he was practice director for AI and deep learning at Think Big Analytics, a Teradata Company, where he mentored and advised Think Big clients and provided guidance on ongoing deep learning projects; he was also a management consultant and a software engineer earlier in his career. A continuous learner, Mo conducts research on applications of deep learning, reinforcement learning, and graph analytics toward solving existing and novel business problems and brings a diversity of educational and hands-on expertise connecting business and technology. He holds an MBA, a master’s degree in computer science, and a bachelor’s degree in mathematics.
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