TensorFlow is mainly developed by Google and released under open source license. We can easily access Tensorflow in Python to create Deep Learning models. I had to use Keras library for Recurrent Neural Networks and found that I need to install Tensorflow to use Keras. I really found the process very tough. For people like me who has very less knowledge about Python or pip or conda, it was a nightmare. Finally, I could solve the issue and installed the package successfully but had to read hundreds of articles and followed more than 10-15 methods, installed Anaconda more than 7-10 times to get the right version. As I have faced all these issues and installed it successfully, I thought of writing about it. Also, one of my dearest friends keeps me pushing to write a blog at least once a week so hopefully this will help me. Though there are hundreds of articles and there is very less chance that my blog would appear in Google search like other articles, but it is worth sharing and writing.
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Few years back, I was working on a Demand forecasting project where I was dealing with thousands of combinations of Brand-SKU-Store combination. The challenge was to improve existing accuracy to provide better forecasts of each SKU at a store level. Even if you are dealing with 100 SKUs and 200 stores there will be 20,000 combinations and you can’t selectively pick a model for each combination. We tried various individual techniques such as ARIMA, ETS, UCM, NN, and many more but couldn’t meet the accuracy benchmark. Then one of my seniors introduced me to the OPERA package and asked me to research and optimize it for our needs. I successfully implemented it and today I will talk about the same package with detailed R code. Continue reading 
I wrote an article on How to 
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