תוכן מסופק על ידי William Monroe. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי William Monroe או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.
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On this episode of Advances in Care , host Erin Welsh and Dr. Craig Smith, Chair of the Department of Surgery and Surgeon-in-Chief at NewYork-Presbyterian and Columbia discuss the highlights of Dr. Smith’s 40+ year career as a cardiac surgeon and how the culture of Columbia has been a catalyst for innovation in cardiac care. Dr. Smith describes the excitement of helping to pioneer the institution’s heart transplant program in the 1980s, when it was just one of only three hospitals in the country practicing heart transplantation. Dr. Smith also explains how a unique collaboration with Columbia’s cardiology team led to the first of several groundbreaking trials, called PARTNER (Placement of AoRTic TraNscatheteR Valve), which paved the way for a monumental treatment for aortic stenosis — the most common heart valve disease that is lethal if left untreated. During the trial, Dr. Smith worked closely with Dr. Martin B. Leon, Professor of Medicine at Columbia University Irving Medical Center and Chief Innovation Officer and the Director of the Cardiovascular Data Science Center for the Division of Cardiology. Their findings elevated TAVR, or transcatheter aortic valve replacement, to eventually become the gold-standard for aortic stenosis patients at all levels of illness severity and surgical risk. Today, an experienced team of specialists at Columbia treat TAVR patients with a combination of advancements including advanced replacement valve materials, three-dimensional and ECG imaging, and a personalized approach to cardiac care. Finally, Dr. Smith shares his thoughts on new frontiers of cardiac surgery, like the challenge of repairing the mitral and tricuspid valves, and the promising application of robotic surgery for complex, high-risk operations. He reflects on life after he retires from operating, and shares his observations of how NewYork-Presbyterian and Columbia have evolved in the decades since he began his residency. For more information visit nyp.org/Advances…
תוכן מסופק על ידי William Monroe. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי William Monroe או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.
The Data Science Club is hosted by Ravi Tripathi and William Monroe from University of Alabama at Birmingham. Every episode we hop in to a different data science application, using freely available code that you can also run on your own.
תוכן מסופק על ידי William Monroe. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי William Monroe או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.
The Data Science Club is hosted by Ravi Tripathi and William Monroe from University of Alabama at Birmingham. Every episode we hop in to a different data science application, using freely available code that you can also run on your own.
This episode of the UAB Data Science Club, we are interviewing Patrick Hall. He has written the book on Machine Learning Interpretability, and is the Senior Director of Product at https://www.h2o.ai/. Patrick guides us through a Disparate Impact Analysis, and we discuss AI security, fairness, and Asimov’s rules of robotics. This is the notebook we looked at with Patrick Hall https://nbviewer.jupyter.org/github/jphall663/interpretable_machine_learning_with_python/blob/master/dia.ipynb Patrick Hall’s Machine Learning Interpretability Book https://www.h2o.ai/oreilly-mli-booklet-2019/ Warning Signs: The Future of Privacy and Security in an Age of Machine Learning https://fpf.org/wp-content/uploads/2019/09/FPF-Indecent-Exposure-Report-Final-digital.pdf Fairness, Accountability, and Transparency in Machine Learning https://www.fatml.org/ IBM AI Fairness 360 Toolkit http://aif360.mybluemix.net/ AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models https://arxiv.org/abs/1909.09251…
This week we are looking at the first 3 notebooks in the kaggle data visualization track: https://www.kaggle.com/learn/data-visualization Visualization is super important to the data scientist, since these are the tools we must use to communicate findings and tell stories with the data we are analyzing.…
Today, Ravi and I cover two more feature extraction techniques, Locally Linear Embedding (LLE) and t-distributed Stochastic Neighbor Embedding (T-SNE). We are building on the notebook we started in video #23, so check that one out if you haven't already. https://towardsdatascience.com/feature-extraction-techniques-d619b56e31be…
In this video, Ravi and I go over some basic feature extraction and dimensionality reduction techniques. Here is the tutorial we used. https://towardsdatascience.com/feature-extraction-techniques-d619b56e31be Next week we will do the second half of this article.
Today, Ravi and I are using Python and the keras library to explore training convolutional neural networks with starting, stopping, and resuming training. We are going through https://www.pyimagesearch.com/2019/09/23/keras-starting-stopping-and-resuming-training/?utm_source=facebook&utm_medium=ad-23-09-2019&utm_campaign=23+September+2019+BP+-+Traffic&utm_content=Default+name+-+Traffic&fbid_campaign=6122406376646&fbid_adset=6122407684846&utm_adset=23+September+2019+BP+-+Email+List+-+United+States+-+18%2B&fbid_ad=6122407685046 We will be using the environment we created in the first Data Science Club video: https://youtu.be/Ew6kAP_6PBI, so if you haven't already, do that one first! Please Like and Subscribe if you would like to get these videos as we release them. Feel free to ask any questions here or in office hours…
Today, Ravi and I are using Python and the keras library to explore generative Adversarial networks. This was a head scratcher for sure. Since it was the first time we had played around with generative adversarial networks there were a number of hard concepts we waddled through. We are going through https://machinelearningmastery.com/how-to-develop-a-generative-adversarial-network-for-a-1-dimensional-function-from-scratch-in-keras/ Jason Brownlee (the author of the post) has a whole book on using GANs, so check that out too if you are interested. We will be using the environment we created in the first Data Science Club video, so if you haven't already, do that one first! Please Like and Subscribe if you would like to get these videos as we release them. Feel free to ask any questions here or in office hours…
Today, Ravi and William are using Python and the keras library to explore convolutional neural networks for image classification. We are going through https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py We will be using the environment we created in the first Data Science Club video, so if you haven't already, do that one first! Please Like and Subscribe if you would like to get these videos as we release them. Feel free to ask any questions here or in office hours…
Hey Y'all, This is just some synths we through together for some bumper music at the beginning of the episode while we wait for streaming to get going :).
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Player FM סורק את האינטרנט עבור פודקאסטים באיכות גבוהה בשבילכם כדי שתהנו מהם כרגע. זה יישום הפודקאסט הטוב ביותר והוא עובד על אנדרואיד, iPhone ואינטרנט. הירשמו לסנכרון מנויים במכשירים שונים.