Intro to Deep Learning

The emerging field of precision medicine demands a new approach to research that harnesses the power of data science. This requires biomedical researchers and practicing clinicians with skills in quantitative and computer sciences as well as modern computing and data infrastructure to support relevant research activities and implement findings in clinical practice. TheUCSF Bakar Computational Health Sciences Instituteis working to build a foundation of faculty and knowledge assets in computational health sciences and bring together a community of thinkers interested in this emerging field. Our initiatives are focused on enhancing education and infrastructure as well as building community with the goal of advancing computational health sciences in research, practice, and education—in support of precision medicine for all.TheUC Berkeley D-Labis a highly-popular campus-wide data science hub, providing services, support, and a venue for cutting-edge research design and experimentation in data-intensive social science. D-Lab scientific and technical staff provide training and consultation, and host interdisciplinary working groups on a wide range of data science methods from Python andRfundamentals, to machine learning and text analysis/natural language processing, to data visualization and curation. The D-Lab team also provides access to a rich array of state and federal data resources, supports restricted data services, and advises on technical questions and research design. More at http://dlab.berkeley.edu/. UCSF trainees welcome!

UCSF Bakar Computational Health Sciences Institute & UC Berkeley D-Lab

UCSF Mission Bay Campus

The UCSF Bakar Institute is partnering with the <a href="https://dlab.berkeley.edu/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">UC Berkeley D-Lab</a> to offer an introductory deep learning workshop for UCSF faculty, students, and staff.This workshop introduces the basic concepts of Deep Learning - the training and performance evaluation of large neural networks. Like many other machine learning algorithms, we will use deep learning algorithms to map input data to their appropriately classified outcome labels. You will use the R interface to Keras to become familiar with basic concepts like input and output layers, batch sizes and output dimensions, dropout rates, weight parametrization and bias, backpropagation, and loss, activation, and optimization functions for image classification applications in biomedicine. You will also gain confidence exploring more complex approaches that utilize pretrained and fine-tuned models.By registering to attend, you are agreeing to provide feedback on the workshop to the organizers in follow-up communications.About the Instructors:Evan Muzzall earned his PhD in Biological Anthropology from Southern Illinois University Carbondale where he focused on spatial patterns of skeletal and dental variation in two large necropoles of Iron Age Central Italy (1st millennium BC). His current research focuses on how environmental and cultural influences affect &quot;normal&quot; skeletal and dental developmental trajectories and various machine learning topics. He is the Instructional Services Lead at the D-Lab, teaches several of the R workshop trainings, and helped found the <a href="https://dlab.berkeley.edu/working-groups/machine-learning-working-group-0" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Machine Learning Working Group</a> in Fall 2016.Chris Kennedy is a PhD student in biostatistics at UC Berkeley, and an independent data science consultant. He is also a D-Lab instructor and consultant, a Berkeley Institute for Data Science Fellow, a <a href="http://bbd.berkeley.edu" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Biomedical Big Data Fellow,</a> and a graduate researcher in the Integrative Cancer Research Group. His methodological interests encompass machine learning, randomized trials, targeted causal inference, deep learning, &amp; text analysis. His applications are primarily in precision medicine, public health, genomics, and election campaigns.

Intro to Deep Learning

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Intro to Deep Learning

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