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Machine Learning: Overviews


Which Python Data Science Package Should I Use When?

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Every package you'll see is free and open source software. Thank you to all the folks who create, support, and maintain these projects! If you're interested in learning about contributing fixes to open source projects, here's a good guide. And If you're interested in the foundations that support these projects, I wrote an overview here. Pandas is a workhorse to help you understand and manipulate your data.


Chest CT Scan Machine Learning in 5 minutes

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This post provides an overview of chest CT scan machine learning organized by clinical goal, data representation, task, and model. A chest CT scan is a grayscale 3-dimensional medical image that depicts the chest, including the heart and lungs. CT scans are used for the diagnosis and monitoring of many different conditions including cancer, fractures, and infections. The clinical goal refers to the medical abnormality that is the focus of the study. Many CT machine learning papers focus on lung nodules.


Artificial Intelligence in Healthcare

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Artificial intelligence refers to simulating the behavior of humans, so that machines can be programmed to perform intelligent behavior and mimic human actions. It is a branch of computer science dealing with building smart machines which can perform actions, typically needing human intelligence. With the availability of huge data, faster computation power, and technology advancement in machine learning and deep learning is providing a paradigm shift in across all the sectors. Artificial Intelligence (AI) in healthcare leverages complex algorithms to emulate human behavior in the data exploration, analysis and training the models, and comprehension of complicated medical and healthcare data. In this article, we will review the key applications of artificial intelligence in the healthcare sector.


Artificial Intelligence in Healthcare

#artificialintelligence

Artificial intelligence refers to simulating the behavior of humans, so that machines can be programmed to perform intelligent behavior and mimic human actions. It is a branch of computer science dealing with building smart machines which can perform actions, typically needing human intelligence. With the availability of huge data, faster computation power, and technology advancement in machine learning and deep learning is providing a paradigm shift in across all the sectors. Artificial Intelligence (AI) in healthcare leverages complex algorithms to emulate human behavior in the data exploration, analysis and training the models, and comprehension of complicated medical and healthcare data. In this article, we will review the key applications of artificial intelligence in the healthcare sector.


Top 15 AI Articles You Should Read This Month - July 2020

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Usually, every month we write an article about the best and most promising AI research papers from that month. In addition to that, we list fifteen AI articles we have found amazing that month. This collection of articles should give you an overview of what happened that month in the AI industry both from technical, business and from an ethical perspective. Are you afraid that AI might take your job? Make sure you are the one who is building it.


Artificial Intelligence in the Pharmaceutical Industry - An Overview of Innovations

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Ayn serves as AI Analyst at Emerj - covering artificial intelligence use-cases and trends across industries. She previously held various roles at Accenture. Several factors have contributed to the advancement of AI in the pharmaceutical industry. These factors include the increase in the size of and the greater variety of types of biomedical datasets, as a result of the increased usage of electronic health records. This article intends to provide business leaders in the pharmacy space with an idea of what they can currently expect from Ai in their industry.


Graph signal processing for machine learning: A review and new perspectives

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The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains such as networks and graphs, are one of the key questions in modern machine learning. Graph signal processing (GSP), a vibrant branch of signal processing models and algorithms that aims at handling data supported on graphs, opens new paths of research to address this challenge. In this article, we review a few important contributions made by GSP concepts and tools, such as graph filters and transforms, to the development of novel machine learning algorithms. In particular, our discussion focuses on the following three aspects: exploiting data structure and relational priors, improving data and computational efficiency, and enhancing model interpretability. Furthermore, we provide new perspectives on future development of GSP techniques that may serve as a bridge between applied mathematics and signal processing on one side, and machine learning and network science on the other.


[D] Quality Contributions Roundup 7/22

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The rest of the thread, Tell me about a paper that you found inspiring, from u/mitare is also quite interesting. This paper is a really comprehensive review detailing what exactly current ML techniques are unable to do that humans can do very well. It lays the groundwork that needs to be done to make human-level artificial intelligence.


Using Machine Learning To Automate Data Coding At The Bureau Of Labor Statistics (BLS)

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Government agencies are awash in documents. Many of these documents are paper-based, but even for the electronic documents a human is still often needed to process and understand those documents to make use of them for vital services. Federal agencies are increasingly looking to AI to help improve those document and human-bound processes by applying advanced machine learning, neural network, and natural language processing (NLP) technologies. While for many these technologies might be fairly new in their organization, in some government agencies, they have been using that technology for many years, augmenting and enhancing various workflows and tasks. In the case of the Bureau of Labor Statistics (BLS), the agency is mandated to conduct a Survey of Occupational Injuries and Illnesses to determine workplace injuries and help guide policy.


What is AI

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AI is the superset of various techniques that allow machines to be artificially intelligent. Machine learning refers to a machine's ability to think without being externally programmed. While devices have traditionally been programmed with a set of rules for how to act, machine learning enables devices to learn directly from the data itself and become more intelligent over time as more data is collected. Deep learning is a machine learning technique that uses multiple neural network layers to progressively extract higher level features from the raw input data. For example, in image processing, lower layers of the neural network may identify edges, while higher layers may identify the concepts relevant to a human such as letters or faces.