data science


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.


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.


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.


Deep Learning Performance Cheat Sheet – Towards Data Science

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The question that I get the most from new and experienced machine learning engineers is "how can I get higher accuracy?" Makes a lot of sense since the most valuable part of machine learning for business is often its predictive capabilities. Improving the accuracy of prediction is an easy way to squeeze more value from existing systems. The guide will be broken up into four different sections with some strategies in each. Not all of these ideas will boost performance, and you will see limited returns the more of them you apply to the same problem.


Taking Deep Q Networks a step further – Towards Data Science

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Today's topic is … well, the same as the last one. Last time, we explained what Q Learning is and how to use the Bellman equation to find the Q-values and as a result the optimal policy. Later, we introduced Deep Q Networks and how instead of computing all the values of the Q-table, we let a Deep Neural Network learn to approximate them. Deep Q Networks take as input the state of the environment and output a Q value for each possible action. The maximum Q value determines, which action the agent will perform.


New algorithm can more quickly predict LED materials

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Researchers from the University of Houston have devised a new machine learning algorithm that is efficient enough to run on a personal computer and predict the properties of more than 100,000 compounds in search of those most likely to be efficient phosphors for LED lighting. 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.


Driving financial inclusion using Artificial Intelligence

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Artificial Intelligence tools are rapidly changing how financial institutions operate, manage data, and interact with customers. The revolution brought by the AI – a blend of three advanced technologies: machine learning, natural language processing and cognitive computing – has huge implications for the financial services industry in Nigeria. According to Microsoft Nigeria Country Manager, Mr Akin Banuso, with the use of modern tools like Microsoft's Azure Machine Learning platform, Financial Service Providers can crunch large volumes of data faster and more accurately, which considerably lessens time-to-market to deliver products and services. "The AI has the potential to advance nearly every field of human endeavour and address countless societal challenges. This is why we are investing in not only making the technology more accessible, but also building capacity in the use of machine learning concepts to address analytical gaps in financial inclusion and other areas," Banuso says.


DataCamp's Data Science And Machine Learning Programs: A Review

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One of my favorite places to learn data science is an under-the-radar educational website, DataCamp. DataCamp doesn't get nearly the attention that some of the larger, more well-funded online coding schools get, but, I often find myself on one of their tutorials whenever I'm learning something new related to statistics or machine learning. Over the past few months, I've dedicated at least a few hours a week to learning the underpinnings of automation and, where I find something interesting, to blog about my experience. Unlike almost every other school or tutorial I've encountered, DataCamp has a delightfully distinct and powerful approach to education: every single piece of instruction is paired with a simple example and interactive tutorial. There are no long lectures; there are no complicated diagrams.


TensorTask - Artificial Intelligence Markets Powered by Stellar

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In many respects, we are reinventing modern programming tools for the A.I. age. Models and expensive resources like talent, data and computing power are currently centralized within large tech corporations. TensorFlow, Tensorflow Hub, AutoML, Algorithmia, and cloud computing are all examples of increasing decentralization of artificial intelligence. Accelerate development (1000 brains are better than 100). Make A.I. safer (more people involved to check and balance development).