Machine Learning



Big Data Science with the BD2K-LINCS Data Coordination and Integration Center Coursera

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About this course: The Library of Integrative Network-based Cellular Signatures (LINCS) is an NIH Common Fund program. The idea is to perturb different types of human cells with many different types of perturbations such as: drugs and other small molecules; genetic manipulations such as knockdown or overexpression of single genes; manipulation of the extracellular microenvironment conditions, for example, growing cells on different surfaces, and more. Most importantly, the course covers computational methods including: data clustering, gene-set enrichment analysis, interactive data visualization, and supervised learning. Finally, we introduce crowdsourcing/citizen-science projects where students can work together in teams to extract expression signatures from public databases and then query such collections of signatures against LINCS data for predicting small molecules as potential therapeutics.


Matrix Factorization and Advanced Techniques Coursera

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About this course: In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.


Understanding overfitting: an inaccurate meme in supervised learning

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It seems like, a kind of an urban legend or a meme, a folklore is circulating in data science or allied fields with the following statement: Applying cross-validation prevents overfitting and a good out-of-sample performance, low generalisation error in unseen data, indicates not an overfit. Aim In this post, we will give an intuition on why model validation as approximating generalization error of a model fit and detection of overfitting can not be resolved simultaneously on a single model. Let's use the following functional form, from classic text of Bishop, but with an added Gaussian noise $$ f(x) sin(2\pi x) \mathcal{N}(0,0.1).$$ We generate large enough set, 100 points to avoid sample size issue discussed in Bishop's book, see Figure 2. Overtraining is not overfitting Overtraining means a model performance degrades in learning model parameters against an objective variable that effects how model is build, for example, an objective variable can be a training data size or iteration cycle in neural network.


Google acquires AIMatter, maker of the Fabby computer vision app

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The search and Android giant has acquired AIMatter, a startup founded in Belarus that has built both a neural network-based AI platform and SDK to detect and process images quickly on mobile devices, and a photo and video editing app that has served as a proof-of-concept of the tech called Fabby. AIMatter had people working in Minsk, the Bay Area, and Zurich (Switzerland being particularly strong in computer vision tech, see here, here, and here). This isn't just for fun apps that let you add dog ears and crazy hair colors to your photos -- although, as Fabby, Prisma, Snapchat, Facebook and countless others have proven, filters can be a lot of fun. But there are also Google's efforts in VR and AR; and its work in building machine learning platforms like TensorFlow for developers to build their own apps using machine learning (AIMatter's SDK could be notable here).


Beware! The AI Machines Are Coming For Your Job

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Other fields such as finance, marketing, and human resource management are increasingly finding use of AI-based solutions for achieving faster results and more accurate analysis. The ability of AI to absorb and crunch humongous amount of information makes it a prime candidate suited for complex decision making in corporate functions such as marketing, human resources, and planning and strategy. Although consultants can offer personalized and insightful advice, AI is competitive in terms of providing informative data analysis and presentation. Machine learning application and AI can analyze all kinds of data and provide the necessary insights in less time compared to a team that will have to read through, compute, and properly present their insights.


A Gentle Intro to TensorFlow for Theano Users -- Lenet in TensorFlow

@machinelearnbot

Welcome to the Lenet tutorial using TensorFlow. From being a long time user of Theano, migrating to TensorFlow is not that easy. This documentation website that comes along with this repository might help users migrating from theano to tensorflow, just as I did while implementing this repository. Once the code is running, setup tensorboard to observe results and outputs.


Neuroscience and Machine Learning Restore Movement in Paralyzed Man's Hand

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The engineers at Battelle worked with physicians and neuroscientists from Ohio State University Wexner Medical Center to develop the research approach and perform the clinical study. In 2014, Ohio State surgeons implanted a chip in Ian's brain. The team used brain imaging to identify and isolate the part of Mr. Burkhart's brain that controls hand movements. Through repetition, the firing patterns were analyzed and used to develop an algorithm to control the muscles in his hand.


Amazon Macie automates cloud data protection with machine learning

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This year, it's Amazon Macie, a security service designed to automatically discover and protect sensitive data stored in AWS. As organizations move more of their data to Amazon's various cloud offerings, security teams have the unenviable task of continuously tracking the data to identify, classify and protect sensitive pieces of information such as personally identifiable information (PII), personal health information (PHI), regulatory documents, API keys, secret key material and intellectual property. As Amazon Macie recognizes personally identifiable information (PII), organizations can use the Macie dashboard to show compliance with GDPR regulations around encryption and pseudonymization of data. In contrast, Microsoft has integrated management tools in its Azure platform and Google offers many security offerings by default in Google Cloud Platform.


Gartner's Hype Cycle for Emerging Technologies, 2017 Adds 5G And Deep Learning For First Time

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The eight technologies added to the Hype Cycle this year include 5G, Artificial General Intelligence, Deep Learning, Deep Reinforcement Learning, Digital Twin, Edge Computing, Serverless PaaS and Cognitive Computing. Ten technologies not included in the hype cycle for 2017 include 802.11ax, The three most dominant trends include Artifical Intelligence (AI) Everywhere, Transparently Immersive Experiences, and Digital Platforms. Gartner believes that key platform-enabling technologies are 5G, Digital Twin, Edge Computing, Blockchain, IoT Platforms, Neuromorphic Hardware, Quantum Computing, Serverless PaaS and Software-Defined Security. Key takeaways from this year's Hype Cycle include the following: