Deep Learning: TensorFlow 2.0 vs PyTorch

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Facilitated by the confluence of inexpensive computing power, unprecedentedly large data sets, and clever theoretical advances, Deep Learning algorithms are driving the contemporary revolution in Artificial Intelligence. Deep Learning has emerged as uniquely influential across a broad range of applications, including classification (e.g., visual recognition, sentiment analysis), prediction (e.g., stock markets, health outcomes), generation (e.g., creating works of art, composing music), and sequential decision-making (e.g., games, robotics). In the past few years, Deep Neural Networks have made their way into countless everyday applications, including Tesla’s Autopilot, Amazon's Alexa, and Google's suggested email replies. Indeed, Deep Learning algorithms have exceeded human performance on previously intractable computational problems like language translation, object detection, and the game of Go.

This talk begins with a survey of the primary families of Deep Learning approaches: Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, and Deep Reinforcement Learning. Via interactive Jupyter notebook demos in Python, the meat of the talk will appraise the two leading Deep Learning libraries: TensorFlow and PyTorch. With respect to both model development and production deployment, the strengths and weaknesses of the two libraries will be covered -- with a particular focus on the upcoming TensorFlow 2.0 release that integrates core TensorFlow with the high-level Keras API. The talk will conclude with Q&A as well as a book-signing session for Deep Learning Illustrated, Dr. Krohn's new book.

Jon Krohn is Chief Data Scientist at the machine learning company untapt. He is the presenter of a popular series of tutorials on artificial neural networks, including Deep Learning with TensorFlow, and is the author of Deep Learning Illustrated, released by Pearson in 2019. Jon holds a doctorate in neuroscience from Oxford University and has been publishing on machine learning in leading academic journals since 2010. He provides a comprehensive deep learning curriculum at the NYC Data Science Academy, guest lectures at Columbia University and, along with researchers from the university's Irving Medical Center, holds a National Institutes of Health grant to automate medical image processing with deep learning.


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