Teaching Methods


Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients

@machinelearnbot

Patients have had heart failure (HF) for centuries, and it is estimated that more than 37 million people worldwide are currently affected.1 Despite being a complex clinical syndrome, contemporary clinical descriptors lag far behind its nuanced scientific understanding. In fact, current classifications used clinically and in trials rely heavily on incomplete descriptors such as left ventricular ejection fraction (LVEF) cut points, stratifying patients simply as those with "reduced" or "preserved" LVEF: HFrEF and HFpEF.2 There is increasing recognition that such classifications are discordant with the current understanding of HF and may impair our ability to personalize risk assessment and treatment. The emphasis on LVEF is particularly notable as prior studies have shown only modest differences in long‐term survival among patients with "reduced" as compared with "preserved" LVEF.3, 4 Still further, numerous promising therapies have failed to demonstrate benefit in clinical trials where inclusion was based almost exclusively on LVEF.5 Despite this, recent guidelines have recommended even further subclassification of HF according to LVEF, with the introduction of HF with "midrange ejection fraction" as a distinct clinical entity.6


Basics of Mathematical Notation for Machine Learning - Machine Learning Mastery

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You cannot avoid mathematical notation when reading the descriptions of machine learning methods. Often, all it takes is one term or one fragment of notation in an equation to completely derail your understanding of the entire procedure. This can be extremely frustrating, especially for machine learning beginners coming from the world of development. You can make great progress if you know a few basic areas of mathematical notation and some tricks for working through the description of machine learning methods in papers and books. In this tutorial, you will discover the basics of mathematical notation that you may come across when reading descriptions of techniques in machine learning.


Implementing Deep Learning Methods and Feature Engineering for Text Data: The Skip-gram Model

@machinelearnbot

Editor's note: This post is only one part of a far more thorough and in-depth original, found here, which covers much more than what is included here. The Skip-gram model architecture usually tries to achieve the reverse of what the CBOW model does. It tries to predict the source context words (surrounding words) given a target word (the center word). Now considering that the skip-gram model's aim is to predict the context from the target word, the model typically inverts the contexts and targets, and tries to predict each context word from its target word. Hence the task becomes to predict the context [quick, fox] given target word'brown' or [the, brown] given target word'quick' and so on.


Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments

arXiv.org Machine Learning

In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course. Top participants were invited to describe their algorithms. In this work, we present eight solutions that used deep reinforcement learning approaches, based on algorithms such as Deep Deterministic Policy Gradient, Proximal Policy Optimization, and Trust Region Policy Optimization. Many solutions use similar relaxations and heuristics, such as reward shaping, frame skipping, discretization of the action space, symmetry, and policy blending. However, each of the eight teams implemented different modifications of the known algorithms.


Transforming Logistics with Self-Learning AI NVIDIA Blog

#artificialintelligence

One of the longest-running challenges in the logistics industry is finding the shortest routes. First articulated in the 1930s, the "traveling salesman problem" seeks to deduce the shortest route connecting a group of cities to ensure optimal use of time and resources. Karim Beguir, co-founder and CEO of London-based AI startup InstaDeep, told GPU Technology Conference attendees this week that GPU-powered deep learning and reinforcement learning may have the answer. Previous efforts to address the traveling salesman problem include optimization solvers, heuristics and Monte Carlo Tree Search algorithms. But, according to Beguir, these approaches all suffer from the same shortcoming: They don't learn.


Understanding Feature Engineering: Deep Learning Methods for Text Data

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Editor's note: This post is only one part of a far more thorough and in-depth original, found here, which covers much more than what is included here. Working with unstructured text data is hard especially when you are trying to build an intelligent system which interprets and understands free flowing natural language just like humans. You need to be able to process and transform noisy, unstructured textual data into some structured, vectorized formats which can be understood by any machine learning algorithm. Principles from Natural Language Processing, Machine Learning or Deep Learning all of which fall under the broad umbrella of Artificial Intelligence are effective tools of the trade. Based on my previous posts, an important point to remember here is that any machine learning algorithm is based on principles of statistics, math and optimization.


Understanding Feature Engineering: Deep Learning Methods for Text Data

@machinelearnbot

Editor's note: This post is only one part of a far more thorough and in-depth original, found here, which covers much more than what is included here. Working with unstructured text data is hard especially when you are trying to build an intelligent system which interprets and understands free flowing natural language just like humans. You need to be able to process and transform noisy, unstructured textual data into some structured, vectorized formats which can be understood by any machine learning algorithm. Principles from Natural Language Processing, Machine Learning or Deep Learning all of which fall under the broad umbrella of Artificial Intelligence are effective tools of the trade. Based on my previous posts, an important point to remember here is that any machine learning algorithm is based on principles of statistics, math and optimization.


The 10 Deep Learning Methods AI Practitioners Need to Apply

@machinelearnbot

Interest in machine learning has exploded over the past decade. You see machine learning in computer science programs, industry conferences, and the Wall Street Journal almost daily. For all the talk about machine learning, many conflate what it can do with what they wish it could do. Fundamentally, machine learning is using algorithms to extract information from raw data and represent it in some type of model. We use this model to infer things about other data we have not yet modeled.


Linear Algebra for Deep Learning - Machine Learning Mastery

#artificialintelligence

Linear algebra is a field of applied mathematics that is a prerequisite to reading and understanding the formal description of deep learning methods, such as in papers and textbooks. Generally, an understanding of linear algebra (or parts thereof) is presented as a prerequisite for machine learning. Although important, this area of mathematics is seldom covered by computer science or software engineering degree programs. In this post, you will discover the crash course in linear algebra for deep learning presented in the de facto textbook on deep learning. Linear Algebra for Deep Learning Photo by Quinn Dombrowski, some rights reserved.


Invacio Delivers Cutting-Edge Level-3 (Self-learning) Artificial Intelligence Technology - Tech Company News

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Q: Could you provide our readers with a brief introduction to Invacio? A: My name is William J.D. West, and I'm the Founder of Invacio. Invacio is a cutting-edge technology company that has created a level-3 (Self-learning) artificial intelligence and is ready to bring it to the global market. Invacio's "Jean" is a state-of-the-art, multi-faceted A.I. system that can work as a stand-alone "market specific" module, or as a bigger network of more than 30 individual modules, working together to create one of the most sophisticated Artificial Intelligence platforms on Earth. Invacio brings together the world's largest repository of organized data with the most advanced A.I. technology known to man… and is working to make it accessible to EVERYONE on the planet.