New York, NY
Getting started with PyTorch
Who is this presentation for?
- Data scientists and application developers
Level
Prerequisite knowledge
- Familiarity with Python
- Knowledge of machine learning terms and concepts
Materials or downloads needed in advance
We will use Google Colab during the tutorial. If you are not familiar with Colab, the minimum requirements are that you have a Google account (notably Google Drive) and a modern browser. For more information and pre-tutorial testing, please view the Colab introductory page here: https://colab.research.
What you'll learn
- Learn PyTorch concepts and how to build machine learning models in PyTorch
- Understand image classification, text classification, linear modeling, and transfer learning
- Gain experience working with datasets in PyTorch for machine learning
Description
Since its 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 its 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.
Mo Patel leads a deep dive into all aspects of the PyTorch lifecycle via hands-on examples such as image classification, text classification, and linear modeling. Along the way, you’ll explore other aspects of machine learning such as transfer learning, data modeling, and deploying to production with immersive labs.
Mo Patel
Independent
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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Comments
Ram Vee, if you have Silver or Gold conference pass it includes tutorials there is no additional registration required. Although there is no reserved seating or reservations, you can add the tutorial to your schedule (via conference app or web) to find out updates about the tutorial.
How to I register for this ? I paid for it but not able to see the registration
David, Thank you for inquiring and reminding me to update the “Materials or downloads needed in advance” section. We will be using Google Colab so nothing to install just Google account with Google Drive space and a modern browser. Shortly the text in the description will be updated with these details.
Hi, Mo. What should we install on our laptops prior to the session?