This workshop will introduce participants to Generative Adversarial Networks (GANs). We will demonstrate the core techniques of GANs, including how to use Deep Convolutional GANs (DCGANs) to generate images using PyTorch. A basic understanding of the topics covered in previous sessions is assumed. Having working experience with Python, Jupyter Notebooks, and linear algebra will be…
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deep learning
Learning Convolutional Neural Networks (2)
This workshop will be the second lecture in our introduction to convolutional neural networks (CNNs). We will continue our learning by applying data augmentation and transfer learning techniques to improve our solution for the classic Dogs-vs-Cats problem using PyTorch. A basic understanding of the topics covered in previous sessions is assumed. Having working experience with…
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Learning Convolutional Neural Networks (1)
This workshop will provide an introduction to convolutional neural networks (CNNs). We will begin by using PyTorch to perform image processing on the classic Dogs-vs-Cats problem. Basic knowledge of the topics covered in previous sessions is assumed. Having working experience with Python, Jupyter Notebooks, and linear algebra will be helpful for fully participating in the…
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Deep Learning, the Good, the Bad and the Ugly
In this workshop, we will talk about deep learning and neural network from a different perspective. After a very brief introduction to what machine learning and deep learning are, our discussion will be focused on what deep learning technology can (and cannot) bring to academic research, including both the benefits and the challenges. No specific…
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Learning PyTorch
We will give a general introduction to PyTorch, a popular deep learning framework, with practical illustrations on the primary usage of tensors and automatic differentiation, and on solving a simple temperature-conversion problem using PyTorch. The knowledge of topics covered in the previous session about machine/deep learning is assumed. Working experience with Python and Jupyter Notebooks…
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Introduction to Neural Networks
This workshop is a descriptive “no-math” and “no-python” introduction to what deep learning is and how to train a deep neural network. Our discussion will be arranged along with a general procedure for working on a deep learning project. Basic knowledge of calculus and linear algebra will be helpful in understanding the details. Any questions…
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Learning Convolutional Neural Networks (2)
Workshop will be conducted in Zoom in PST time. Please Register here in advance for this lecture. This workshope will be the second lecture on the introduction to convolutional neural network. We will continue our learning on how to apply data augmentation and transfer learning techniques to get a better solution for the classic Dogs-vs-Cats…
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Deep Learning in Medicine
Recent years have witnessed a dramatic resurgence of interest in applying deep learning in various research and application areas. In this 2 hour session, we will present a broad and high-level overview on what deep-learning technologies can do for the domains of medicine and healthcare. Our discussion will focus on the three major application fields:…
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Learning Deep Learning with PyTorch (6) Recurrent Neural Networks and LSTM
This workshop series is to present overviews to the exciting deep learning techniques and to provide a practical guide for general audience to step into the field. It will be primarily appropriate for the beginners who want to learn the techniques and apply to their future research activities. Researchers with deep learning experiences are expected…
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Learning Deep Learning with PyTorch (5) Data Augmentation and Transfer Learning
This workshop series is to present overviews to the exciting deep learning techniques and to provide a practical guide for general audience to step into the field. It will be primarily appropriate for the beginners who want to learn the techniques and apply to their future research activities. Researchers with deep learning experiences are expected…
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