PPT-An Introduction to Deep Transfer Learning

Mohammadreza Ebrahimi Hsinchun Chen October 29 2018 1 Acknowledgment Some images and materials are from Dong Wang and Thomas Fang Zheng Tsinghua University Chuanqi Tan Fuchun. Aaron Crandall, 2015. What is Deep Learning?. Architectures with more mathematical . transformations from source to target. Sparse representations. Stacking based learning . approaches. Mor. e focus on handling unlabeled data. Deep Learning. Zhiting. Hu. 2014-4-1. Outline. Motivation: why go deep?. DL since 2006. Some DL Models. Discussion. 2. Outline. Motivation: why go deep?. DL since 2006. Some DL Models. Discussion. 3. DIGITS 1 Introduction to Deep Learning 2 What is DIGITS 3 How to use DIGITS AGENDA Practical DEEP LEARNING Examples Image Classification, Object Detection, Localization, Action Recognition, Scene Un Professor Qiang Yang. Outline. Introduction. Supervised Learning. Convolutional Neural Network. Sequence Modelling: RNN and its extensions. Unsupervised Learning. Autoencoder. Stacked . Denoising. . Continuous. Scoring in Practical Applications. Tuesday 6/28/2016. By Greg Makowski. Greg@Ligadata.com. www.Linkedin.com/in/GregMakowski. Community @. . http. ://. Kamanja.org. . . Try out. Future . Deep . Learning. James K . Baker, Bhiksha Raj. , Rita Singh. Opportunities in Machine Learning. Great . advances are being made in machine learning. Artificial Intelligence. Machine. Learning. After decades of intermittent progress, some applications are beginning to demonstrate human-level performance!. Aaron Crandall, 2015. What is Deep Learning?. Architectures with more mathematical . transformations from source to target. Sparse representations. Stacking based learning . approaches. Mor. e focus on handling unlabeled data. Assistant Professor. Computer Science and Engineering Department. Indian Institute of Technology Kharagpur. http://cse.iitkgp.ac.in/~adas/. Biological Neural Network. Image courtesy: F. . A. . Makinde. Internet Research Initiative 2020-2021. Suraj Vathsa. Develop an Artificial Intelligence (Deep Learning) . enabled system to identify adventitious sounds such . as wheezes and crackles in audio recordings of . Ryota Tomioka (. ryoto@microsoft.com. ). MSR Summer School. 2 July 2018. Azure . iPython. Notebook. https://notebooks.azure.com/ryotat/libraries/DLTutorial. Agenda. This lecture covers. Introduction to machine learning. January 18, 2021. Mohammad Hammoud. Carnegie Mellon University in Qatar. Outline. Introduction. What is AI?. Administrivia. AI Applications in Medicine. On the Verge of Major Breakthroughs. Artificial Intelligence (AI) has been moving extremely quickly in the last few years, demonstrating a potential to revolutionize every aspect of our lives. BackgroundGlaucoma is a kind of chronic disease that damages optic nerve of the eye. Due to the diculty of examination and treatment, patients with glaucoma often suer from visual impairment or even Chair for Computer Aided Medical Procedures & Augmented Reality. Master seminar: Deep Learning for Medical Applications. Tutor: . Shadi. . Albarqouni. , PhD. Student: Panarit Jahiri. Maithra. Raghu, . Transfer Learning. Transfer a model trained on . source. data A to . target . data B. Task transfer: . in this case, . the source and target data can be the same. Image classification -> image segmentation.