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via: https://nmarkou.blogspot.co.uk/2017/02/the-black-magic-of-deep-learning-tips.html I've been using Deep Learning and Deep Belief Networks since 2013. I was involved in a green field project and I was in charge of deciding the core Machine Learning algorithms to be used in a computer vision platform. Nothing worked good enough and if it did it wouldn't generalize, required fiddling all the time and when introduced to similar datasets it wouldn't converge. I was lost. I then caught wind from Academia, the new hype of Deep Learning was here and it would solve everything. I was skeptical, so I read the papers, the books and the notes. I then went and put to work everything I learned.  Suprisingly, it was no hype, Deep Learning works and it works well. However it is such a new concept (even though the foundations were laid in the 70's) that a lot of anecdotal tricks and tips started coming out on how to make the most of it (Alex Krizhevsky covered a lot of them ...

According to  Google , the interest in “Big Data” has been trending up for several years and has really gained steam in the last couple. The purpose of this post is to help highlight the differences between data lakes and data warehouses to help you make an informed decision on how to manage your data. Those of us that are data and analytics practitioners have certainly heard the term and as we begin to discuss big data solutions with customers, the conversation naturally turns to a discussion of data lakes. However, I often find that customers either haven’t heard the term or don’t really have a good understanding of what it means. First, let’s define our terms…  Data Warehouse Wikipedia , defines Data Warehouses as: “…central repositories of integrated data from one or more disparate sources. They store current and historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons.” This ...