• Multivariate Time Series Forecasting with LSTMs in Keras
  Neural networks like Long Short-Term Memory (LSTM) recurrent neural networks are able to almost seamlessly model problems with multiple input variables. This is a great benefit in time series forecasting, where classical linear methods can be difficult to adapt to multivariate or multiple input forecasting problems. In this tutorial, you will discover how you can develop an LSTM model for multivariate time series forecasting in the Keras deep learning library. After completing this tutorial, you will know: How to transform a raw dataset into something we can use for time series forecasting. How to prepare data and fit an LSTM for a multivariate time series forecasting problem. How to make a forecast and rescale the result back into the original units.
  with forecasting lstm multivariate network neural tutorial
• Nej, robotarna gör inte 230 miljoner arbetslösa
  Artikel om framtida jobb och hur våra jobb kommer och går
  with system:unfiled
• 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.
  with ai artificial intelligence
• Object detection with deep learning and OpenCV
  PyImageSearch
  with pyimagesearch
• Object Detection with Intel Neural Compute Stick and YOLO
  Objekterkennung mit neuronalen Netzen - codecentric AG Blog
  with compute intel ncs neural stick yolo
• openwhisk-darkvisionapp
  Discover dark data in videos with IBM Watson and IBM Bluemix OpenWhisk - analysing video feeds
  with bluemix openwhisk videos watson
• 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.
  with artificial github intelligence learning machine predict prices stock
• RNN Training Tips and Tricks
  Monitoring Validation Loss vs. Training Loss If you’re somewhat new to Machine Learning or Neural Networks it can take a bit of expertise to get good models. The most important quantity to keep track of is the difference between your training loss (printed during training) and the validation loss (printed once in a while when the RNN is run on the validation data (by default every 1000 iterations)). In particular: If your training loss is much lower than validation loss then this means the network might be overfitting. Solutions to this are to decrease your network size, or to increase dropout. For example you could try dropout of 0.5 and so on.
  with lstm rnn tips
• Så kommer du igång med AI – innan tåget har gått - CIO Sweden
  Så kommer du igång med AI – innan tåget har gått Nu gäller det att göra sig redo att använda AI och maskininlärning. Här är fem saker som du behöver få på plats innan du kan köra igång.
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• Silicon Valley
  Season 4 Episode 4: Not Hotdog (HBO) - YouTube
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• Snart vet din telefon mer än din familj om hur du mår - IDG.se
  Att våra smarta telefoner snappar upp information om oss är känt sedan länge, men år 2022 kommer telefonerna att känna oss bättre än våra familjer. Det spår analyshuset Gartner i sin trendspaning inför de kommande åren.
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• Sophistication of AI Technologies
  Overview of several artificial technologies available on the market. Siri, Cortana, Alexa, Tay, Watson and others
  with artificial cognitive intelligence learning machine
• 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.
  with artificial github intelligence learning lstm machine market prediction rnn stock
• Stor skillnad mellan mäns och kvinnors hjärnor
  Det finns en biologisk skillnad på mäns och kvinnors hjärnor. Till kvinnornas fördel. Om tusen år kan männen vara helt överflödiga.
  with brain hjärna
• The Forrester New Wave™: Conversational Computing Platforms, Q2 2018
  In Forrester's evaluation of the emerging market for conversational computing platforms, we identified the seven most significant providers — Amazon, Google, IBM, Microsoft, Nuance Communications, Oracle, and Rulai — in the category and evaluated them. This report details our findings about how each vendor scored against nine criteria and where they stand in relation to each other. Application developers should use this review to select the right partners for their conversational computing platform needs.
  with system:unfiled
• Time Series Prediction Using LSTM Deep Neural Networks
  This article focuses on using a Deep LSTM Neural Network architecture to provide multidimensional time series forecasting using Keras and Tensorflow - specifically on stock market datasets to provide momentum indicators of stock price. The code for this framework can be found in the following GitHub repo (it assumes python version 3.5.x and the requirement versions in the requirements.txt file. Deviating from these versions might cause errors): https://github.com/jaungiers/LSTM-Neural-Network-for-Time-Series-Prediction The following article sections will briefly touch on LSTM neuron cells, give a toy example of predicting a sine wave then walk through the application to a stochastic time series. The article assumes a basic working knowledge of simple deep n
  with forecasting keras lstm network neural price stock tensorflow
• 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.
  with artificial intelligence learning machine podcast podcasts
• valohai/qlearning-simple
  Reinforcement learning example
  with system:unfiled
• Why does Keras LSTM batch size used for prediction have to be the same as fitting batch size? - Stack Overflow
  When using a Keras LSTM to predict on time series data I've been getting errors when I'm trying to train the model using a batch size of 50, while then trying to predict on the same model using a batch size of 1 (ie just predicting the next value). Why am I not able to train and fit the model with multiple batches at once, and then use that model to predict for anything other than the same batch size. It doesn't seem to make sense, but then I could easily be missing something about this.
  with batch keras reload save size

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