• Machine Learning Mastery FAQ
  Frequently Asked Questions from machine learning mastery site
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• AI den nya länken till användarna – gör tekniken mänsklig
  CIO Sweden
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• Bigdata@Chalmers Events and Seminars
  Events and presentations from Artificial Intelligence team and Bigdata@Chalmers
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• Deepfakes är det läskigaste på nätet just nu – och ett tydligt exempel på riskerna med AI
  Deepfakes är det läskigaste på nätet just nu – och ett tydligt exempel på riskerna med AI. Blir det fejkade porrfilmer som startar den första konkreta debatten om baksidorna med AI? Deepfakes är nätfenomenet som visar både möjligheterna och riskerna med den snabba utvecklingen inom maskininlärning, skriver Marcus Jerräng.
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• Gartners spådomar om framtiden gör mig mörkrädd
  Fake news är ett fenomen som bara blir värre. Med ny teknik kan vi skapa film och bilder som inte går att genomskåda – men som är falska. Inte ens artificiell intelligens kan avgöra vad som är förfalskat. Det förutspår Gartners analytiker. Och CS reporter Karin Lindström blir mörkrädd.
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• Google AIY Projects
  Google Artifical Intelligence Yourself (AIY) project homepage.
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• How does a closed loop artificial pancreas work when you DIY? Or
  #DIYPS closed loop is working! | Scott, Dana, and #DIYPS
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• How to Develop LSTM Models for Time Series Forecasting
  Long Short-Term Memory networks, or LSTMs for short, can be applied to time series forecasting. There are many types of LSTM models that can be used for each specific type of time series forecasting problem. In this tutorial, you will discover how to develop a suite of LSTM models for a range of standard time series forecasting problems.
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• Nu börjar företagen skala upp sin AI – sju tips för att lyckas - CIO Sweden
  Förra året var året då många testade maskininlärning och automatiseringsverktyg för att skapa bättre kundrelationer, vässa nätverket eller höja sin it-säkerhet. Och många har fått råg i ryggen av sina piloter och är nu redo för att på allvar föra in AI i verksamheten. Enligt en ny undersökning från Pricewaterhousecoopers, PWC, uppger en femtedel av tusen tillfrågade företagsledare i USA att de planerar att implementera AI på allvar på sina företag i år.
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• Predicting blood glucose using Tensorflow
  johnmartinsson/blood-glucose-prediction
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• Predicting Stock Price with LSTM - Towards Data Science
  Machine learning has found its applications in many interesting fields over these years. Taming stock market is one of them. I had been thinking of giving it a shot for quite some time now; mostly to solidify my working knowledge of LSTMs. And finally I have finished the project and quite excited to share my experience.
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• Sophistication of AI Technologies
  Overview of several artificial technologies available on the market. Siri, Cortana, Alexa, Tay, Watson and others
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• Stanford Machine Learning | Coursera
  Machine Learning course from Stanford University @ coursera
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• Stock Market Prediction by Recurrent Neural Network on LSTM Model
  The art of forecasting stock prices has been a difficult task for many of the researchers and analysts. In fact, investors are highly interested in the research area of stock price prediction. For a good and successful investment, many investors are keen on knowing the future situation of the stock market. Good and effective prediction systems for stock market help traders, investors, and analyst by providing supportive information like the future direction of the stock market. In this work, we present a recurrent neural network (RNN) and Long Short-Term Memory (LSTM) approach to predict stock market indices.
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• Top 10 Best Podcasts on AI, Analytics, Data Science, Machine Learning
  The following KDnuggets Top 10 list highlights the most active and popular podcasts so far in 2019 that feature data science and machine learning conversations. We reviewed many more podcasts, so this list drills down to only those published on iTunes with the highest ratings (4.5+), the most reviews, and at least one recent episode within the current month. All show descriptions are adapted from the podcast listing.
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