machine_learning

Proceedings of the 2003 International Conference on Machine Learning and Cybernetics [electronic journal].

IEEE Computer Society




machine_learning

Proceedings of the 2003 International Conference on Machine Learning and Cybernetics [electronic journal].

IEEE Computer Society




machine_learning

Proceedings of the 2003 International Conference on Machine Learning and Cybernetics [electronic journal].

IEEE Computer Society




machine_learning

Machine Learning in High Performance Computing Environments (MLHPC), IEEE/ACM Workshop on [electronic journal].

IEEE / Institute of Electrical and Electronics Engineers Incorporated




machine_learning

Machine Learning and Knowledge Extraction [electronic journal].

Molecular Diversity Preservation International




machine_learning

Applied Machine Learning (ICAML), International Conference on [electronic journal].

IEEE / Institute of Electrical and Electronics Engineers Incorporated




machine_learning

2019 International Conference on Machine Learning and Data Engineering (iCMLDE) [electronic journal].

IEEE / Institute of Electrical and Electronics Engineers Incorporated




machine_learning

2019 International Conference on Applied Machine Learning (ICAML) [electronic journal].

IEEE / Institute of Electrical and Electronics Engineers Incorporated




machine_learning

2019 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC) [electronic journal].

IEEE / Institute of Electrical and Electronics Engineers Incorporated




machine_learning

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) [electronic journal].

IEEE / Institute of Electrical and Electronics Engineers Incorporated




machine_learning

[ASAP] Amorphous Catalysis: Machine Learning Driven High-Throughput Screening of Superior Active Site for Hydrogen Evolution Reaction

The Journal of Physical Chemistry C
DOI: 10.1021/acs.jpcc.0c00406




machine_learning

Machine learning at the Belle II Experiment: the full event interpretation and its validation on Belle data / Thomas Keck

Online Resource




machine_learning

Machine Learning: Hands-On for Developers and Technical Professionals, 2nd Edition


 
Dig deep into the data with a hands-on guide to machine learning with updated examples and more!

Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing



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machine_learning

Next-Generation Machine Learning with Spark: Covers XGBoost, LightGBM, Spark NLP, Distributed Deep Learning with Keras, and More / Butch Quinto

Online Resource




machine_learning

Machine learning and knowledge discovery in databases: International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, proceedings. / Peggy Cellier, Kurt Driessens (eds.)

Online Resource




machine_learning

Machine learning and knowledge discovery in databases: International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, proceedings. / Peggy Cellier, Kurt Driessens (eds.)

Online Resource




machine_learning

Machine learning for intelligent decision science Jitendra Kumar Rout, Minakhi Rout, Himansu Das, editors

Online Resource




machine_learning

Machine learning and information processing: proceedings of ICMLIP 2019 / Debabala Swain, Prasant Kumar Pattnaik, Pradeep K. Gupta, editors

Online Resource




machine_learning

A Web-based Automated Machine Learning Platform to Analyze Liquid Biopsy Data

Lab Chip, 2020, Accepted Manuscript
DOI: 10.1039/D0LC00096E, Paper
Hanfei Shen, Tony Liu, Jesse Cui, Piyush Borole, Ari Benjamin, Konrad Kording, David Issadore
Liquid biopsy (LB) technologies continue to improve in sensitivity, specificity, and multiplexing and can measure an ever growing library of disease biomarkers. However, clinical interpretation of the increasingly large sets...
The content of this RSS Feed (c) The Royal Society of Chemistry




machine_learning

[ASAP] Cov_FB3D: A De Novo Covalent Drug Design Protocol Integrating the BA-SAMP Strategy and Machine-Learning-Based Synthetic Tractability Evaluation

Journal of Chemical Information and Modeling
DOI: 10.1021/acs.jcim.9b01197




machine_learning

[ASAP] LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening

Journal of Chemical Information and Modeling
DOI: 10.1021/acs.jcim.0c00155




machine_learning

[ASAP] Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge

Journal of Chemical Information and Modeling
DOI: 10.1021/acs.jcim.0c00199




machine_learning

[ASAP] Deep Dive into Machine Learning Models for Protein Engineering

Journal of Chemical Information and Modeling
DOI: 10.1021/acs.jcim.0c00073




machine_learning

[ASAP] BIreactive: A Machine-Learning Model to Estimate Covalent Warhead Reactivity

Journal of Chemical Information and Modeling
DOI: 10.1021/acs.jcim.9b01058




machine_learning

Signal processing and machine learning for brain-machine interfaces / edited by Toshihisa Tanaka and Mahnaz Arvaneh

Barker Library - QP360.7.S54 2018




machine_learning

[ASAP] Optimized Multimetal Sensitized Phosphor for Enhanced Red Up-Conversion Luminescence by Machine Learning

ACS Combinatorial Science
DOI: 10.1021/acscombsci.0c00035




machine_learning

Podcast: Watching shoes untie, Cassini’s last dive through the breath of a cryovolcano, and how human bias influences machine learning

This week, walk like an elephant—very far, with seeds in your guts, Cassini’s mission to Saturn wraps up with news on the habitability of its icy moon Enceladus, and how our shoes manage to untie themselves with Online News Editor David Grimm. Aylin Caliskan joins Sarah Crespi to discuss how biases in our writing may be perpetuated by the machines that learn from them. Listen to previous podcasts. Download the show transcript. Transcripts courtesy of Scribie.com. [Image: NASA/JPL-Caltech; Music: Jeffrey Cook]




machine_learning

Machine learning for signal processing : data science, algorithms, and computational statistics / Max A. Little

Little, Max A., author




machine_learning

[ASAP] Using Machine Learning to Predict the Dissociation Energy of Organic Carbonyls

The Journal of Physical Chemistry A
DOI: 10.1021/acs.jpca.0c01280




machine_learning

Machine learning in aquaculture: hunger classification of Lates Calcarifer / Mohd Azraai Mohd Razman, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Zahari Taha, Gian-Antonio Susto, Yukinori Mukai

Online Resource




machine_learning

JSJ 278 Machine Learning with Tyler Renelle

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Tyler Renelle is a contractor and developer who has worked in various web technologies like Node, Angular, Rails, and much more. He's also build machine learning backends in Python (Flask), Tensorflow, and Neural Networks.

The JavaScript Jabber panel dives into Machine Learning with Tyler Renelle. Specifically, they go into what is emerging in machine learning and artificial intelligence and what that means for programmers and programming jobs.

This episode dives into:

  • Whether machine learning will replace programming jobs
  • Economic automation
  • Which platforms and languages to use to get into machine learning
  • and much, much more...

Links:

Picks:

Aimee

AJ

Joe

Tyler




machine_learning

JSJ 405: Machine Learning with Gant Laborde

Gant Laborde is the Chief Innovation Officer of Infinite Red who is working on a course for beginners on machine learning. There is a lot of gatekeeping with machine learning, and this attitude that only people with PhDs should touch it. In spite of this, Gant thinks that in the next 5 years everyone will be using machine learning, and that it will be pioneered by web developers. One of the strong points of the web is experimentation, and Gant contrasts this to the academic approach. 

They conversation turns to Gant’s course on machine learning and how it is structured. He stresses the importance of understanding unicode, assembly, and other higher concepts. In his course he gives you the resources to go deeper and talks about libraries and frameworks available that can get you started right away. His first lesson is a splashdown into the jargon of machine learning, which he maps over into developer terms. After a little JavaScript kung fu, he takes some tools that are already out there and converts it into a website.

Chris and Gant discuss some different uses for machine learning and how it can improve development. One of the biggest applications they see is to train the computers to figure monotonous tasks out while the human beings focus on other projects, such as watching security camera footage and identifying images. Gant restates his belief that in the next 5 years, AI will be everywhere. People will grab the boring things first, then they will go for the exciting things. Gant talks about his creation NSFW.js, an open source train model to help you catch indecent content. He and Chris discuss different applications for this technology.

Next, the panel discusses where machine learning can be seen in everyday life, especially in big companies such as Google. They cite completing your sentences in an email for you as an example of machine learning. They talk about the ethics of machine learning, especially concerning security and personal data. They anticipate that the next problem is edge devices for AI, and this is where JavaScript really comes in, because security and privacy concerns require a developer mindset. They also believe that personal assistant devices, like those from Amazon and Google, will become even more personal through machine learning. They talk about some of the ways that personal assistant devices will improve through machine learning, such as recognizing your voice or understanding your accent. 

Their next topic of discussion is authenticity, and how computers are actually incredibly good at finding deep fakes. They discuss the practice of placing passed away people into movies as one of the applications of machine learning, and the ethics surrounding that. Since developers tend to be worried about inclusions, ethics, and the implications of things, Gant believes that these are the people he wants to have control over what AI is going to do to help build a more conscious data set. 

The show concludes with Gant talking about the resources to help you get started with machine learning. He is a panelist on upcoming DevChat show, Adventures in Machine Learning. He has worked with people with all kinds of skill sets and has found that it doesn’t matter how much you know, it matters how interested and passionate you are about learning. If you’re willing to put the pedal to the metal for at least a month, you can come out with a basic understanding. Chris and Gant talk about Tensorflow, which helps you take care of machine learning at a higher level for fast operations without calculus. Gant is working on putting together a course on Tensorflow. If you’re interested in machine learning, go to academy.infinite.red to sign up for Gant’s course. He also announces that they will be having a sale on Black Friday and Cyber Monday.

Panelists

  • Christopher Buecheler

With special guest: Gant Laborde

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Christopher Buecheler:

Gant Laborde: 

Free 5 day mini course on academy.infinite.red




machine_learning

[ASAP] Nonintrusive Monitoring of Mental Fatigue Status Using Epidermal Electronic Systems and Machine-Learning Algorithms

ACS Sensors
DOI: 10.1021/acssensors.9b02451




machine_learning

[ASAP] Lithium Ion Conduction in Cathode Coating Materials from On-the-Fly Machine Learning

Chemistry of Materials
DOI: 10.1021/acs.chemmater.9b04663




machine_learning

[ASAP] Toward Designing Highly Conductive Polymer Electrolytes by Machine Learning Assisted Coarse-Grained Molecular Dynamics

Chemistry of Materials
DOI: 10.1021/acs.chemmater.9b04830




machine_learning

Process monitoring and feedback control using multiresolution analysis and machine learning




machine_learning

A study of machine learning performance in the prediction of juvenile diabetes from clinical test results




machine_learning

Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes / Arjun Panesar

Online Resource




machine_learning

Machine learning with health care perspective: machine learning and healthcare / Vishal Jain, Jyotir Moy Chatterjee, editors

Online Resource




machine_learning

Machine Learning for iOS Developers


 

Harness the power of Apple iOS machine learning (ML) capabilities and learn the concepts and techniques necessary to be a successful Apple iOS machine learning practitioner!

Machine earning (ML) is the science of getting computers to act without being explicitly programmed. A branch of Artificial Intelligence (AI), machine learning techniques offer ways to identify trends, forecast behavior, and make recommendations. The Apple iOS Software Development



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machine_learning

The demand for life insurance: dynamic ecological systemic theory using machine learning techniques / Wookjae Heo

Online Resource




machine_learning

Verge Genomics raises $32 million for machine learning-backed neuroscience drug discovery