Machine Learning


Machine Learning Fun and Easy - YouTube

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Welcome to the Fun and Easy Machine learning Course in Python and Keras. Are you Intrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventure into the amazing of field Machine Learning. Each section consists of fun and intriguing white board explanations with regards to important concepts in Machine learning as well as practical python labs which you will enhance your comprehension of this vast yet lucrative sub-field of Data Science.


Variational Autoencoders Explained – Towards Data Science

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If such a model is trained on natural looking images, it should assign a high probability value to an image of a lion. An image of random gibberish on the other hand should be assigned a low probability value. The VAE model can also sample examples from the learned PDF, which is the coolest part, since it'll be able to generate new examples that look similar to the original dataset! The input to the model is an image in a 28 28 dimensional space (ℝ[28 28]). The model should estimate a high probability value if the input looks like a digit.


Microsoft Blends IoT And Edge Computing With AI To Change The Game Of Cricket

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Spektacom, a sports tech startup founded by Anil Kumble, one of the most accomplished cricketers in India, partnered with Microsoft to bring cutting-edge technology to the game of cricket. Spektacom built a platform that includes a 5-gram sticker that attaches itself to the cricket bat, a stump box that acts as an IoT gateway, and AI-powered analytics to deliver insights on the batting style of a batsman. The data collected in the cloud is instantly run through a machine learning model that assesses the quality of a shot. Anil officially calls the IoT-enabled bat as a power bat, which doesn't deviate from the specifications of a standard cricket bat. The technology behind the power bat is fascinating.


Farmers Insurance Accelerates Its Impact with AI – MIT Initiative on the Digital Economy – Medium

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Insurance, which is an industry made up of data, transactions, and decisions, is one of the industries that is most likely to be affected by artificial intelligence (AI). Farmers Insurance, the California-based insurer, intends to apply the technology aggressively to its business. AI projects have proliferated around the company over the past few years, and now its leaders are viewing AI as an enterprise capability and are organizing to allow for greater impact. AI is being used in a wide variety of use cases at Farmers. A recent Wall Street Journal article, for example, described the use of AI-based image recognition in the automobile insurance claims process at the company.


Loud ML 1.5 technology roadmap

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Do you want to see more videos on the channel? Let us know what you think, and what you need! 3. Overview Loud ML 1.5 feature set in the roadmap DISCLAIMER: The following information is being shared in order to outline some of our current product plans, but like everything else in life, even the best laid plans get put to rest. We are hopeful that the following can shed some light on our roadmap, but it is important to understand that it is being shared for INFORMATIONAL PURPOSES ONLY, and not as a binding commitment. Please do not rely on this information in making purchasing decisions because ultimately, the development, release, and timing of any products, features or functionality remains at our sole discretion, and is subject to change. Old training data is lost.


Reading China: Predicting policy change with machine learning - AEI

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For the first time in the literature, we develop a quantitative indicator of the Chinese government's policy priorities over a long period of time, which we call the Policy Change Index (PCI) of China. The PCI is a leading indicator of policy changes that runs from 1951 to the third quarter of 2018, and it can be updated in the future. It is designed with two building blocks: the full text of the People's Daily -- the official newspaper of the Communist Party of China -- as input data and a set of machine learning techniques to detect changes in how this newspaper prioritizes policy issues. Due to the unique role of the People's Daily in China's propaganda system, detecting changes in this newspaper allows us to predict changes in China's policies. The construction of the PCI does not require the researcher's understanding of the Chinese context, which suggests a wide range of applications in other settings, such as predicting changes in other (ex-)Communist regimes' policies, measuring decentralization in central-local government relations, quantifying media bias in democratic countries, and predicting changes in lawmaker's voting behavior and in judges' ideological leaning.


Synechron Launches AI Data Science Accelerators for the BFSI sector

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Synechron the global financial services consulting and technology services provider, has announced the launch of its AI Data Science Accelerators for Financial Services, Banking and Insurance (BFSI) firms. These four new solution accelerators help financial services and insurance firms solve complex business challenges by discovering meaningful relationships between events that impact one another (correlation) and cause a future event to happen (causation). Following the success of Synechron's AI Automation Program – Neo, Synechron's AI Data Science experts have developed a powerful set of accelerators that allow financial firms to address business challenges related to investment research generation, predicting the next best action to take with a wealth management client, high-priority customer complaints, and better predicting credit risk related to mortgage lending. The Accelerators combine Natural Language Processing (NLP), Deep Learning algorithms and Data Science to solve the complex business challenges and rely on a powerful Spark and Hadoop platform to ingest and run correlations across massive amounts of data to test hypotheses and predict future outcomes. The Data Science Accelerators are the fifth Accelerator program Synechron has launched in the last two years through its Financial Innovation Labs (FinLabs), which are operating in 11 key global financial markets across North America, Europe, Middle East and APAC; including: New York, Charlotte, Fort Lauderdale, London, Paris, Amsterdam, Serbia, Dubai, Pune, Bangalore and Hyderabad.


Generalized Graph Networks ups Deep Learning to next level AI - NextBigFuture.com Generalized Graph Networks ups Deep Learning to next level AI

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Deep Learning and Machine Learning has made breakthroughs in recent years. There is tens of billions of dollars going into development of the new AI. Google and Deep Mind are recognizing that Deep Learning is not going to reach human cognition. They propose using models of networks to find relations between things to enable computers to generalize more broadly about the world. Deep learning faces challenges in complex language and scene understanding, reasoning about structured data, transferring learning beyond the training conditions, and learning from small amounts of experience.


Machine learning uncovers dementia subtypes with implication for drug trials

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Machine learning could help to find new treatments for dementia, according to researchers at UCL. A new algorithm that can automatically disentangle different patterns of progression in patients with a range of different dementias, including Alzheimer's disease, will enable individuals to be identified that may respond best to different treatments. For the paper, published in Nature Communications, researchers devised and applied a new algorithm called SuStaIn (Subtype and Stage Inference) to routinely acquired MRI scans from patients with dementia. The algorithm was able to identify three separate subtypes of Alzheimer's disease, which broadly match those observed in post-mortems of brain tissue, and several different subtypes of frontotemporal dementia. Critically, however, this subtyping could be done in life, using brain scanning, and very early in the disease process.


New algorithm can more quickly predict LED materials: Researchers report machine learning speeds discovery of new materials

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They then synthesized and tested one of the compounds predicted computationally -- sodium-barium-borate -- and determined it offers 95 percent efficiency and outstanding thermal stability. Jakoah Brgoch, assistant professor of chemistry, and members of his lab describe the work a paper published Oct. 22 in Nature Communications. The researchers used machine learning to quickly scan huge numbers of compounds for key attributes, including Debye temperature and chemical compatibility. Brgoch previously demonstrated that Debye temperature is correlated with efficiency. LED, or light-emitting diode, based bulbs work by using small amounts of rare earth elements, usually europium or cerium, substituted within a ceramic or oxide host -- the interaction between the two materials determines the performance.