So I've built a (relatively) simple web app with a deep learning image classifier, and I have it running on localhost. How do I upload this to the web so that I can link to it from my website? The usage will not be very high at all, but the model needs GPU so it would be better if it's a pay/hour used or something similar. What are the best services to use to do this (as cheap as possible)? …
I just finished PhD and initially wished to work on data science and deep learning. However, after some rounds of interviews, I have been offered a job of web analytics and business intelligence at a medium size company. Is there any similarity with data science, and is there a future in it? Because of some precarious situation, I have to accept this job, but should I keep looking for another job meanwhile, or will the experience be helpful to rise …
Question: What is the Time Decay formula that web analytics packages use to distribute credit across the multiple sessions associated with a user before a conversion? Context: All web analytics packages like Google Analytics rely on the concept of: USERS visitors, ie unique cookied browsers. Users have many... SESSIONS visits, ie sets of pageviews within close time proximity. CONVERSIONS a success event such as a sign-up which can occur during a session. These analytics packages allow you to distribute the …
I'm looking to find some tutorials involving embedding a Machine Learning Model into a Web Application eventualy with some storage and database behind( with things like slack etc) .
I've just started reading about AB testing, as it pertains to optimizing website design. I find it interesting that most of the methods assume that changes to the layout and appearance are independent of each other. I understand that the most common method of optimization is the 'multi-armed bandit' procedure. While I grasp the concept of it, it seems to ignore the fact that changes (changes to the website in this case) are not independent to each other. For example, …
The most popular use case seem to be recommender systems of different kinds (such as recommending shopping items, users in social networks etc.). But what are other typical data science applications, which may be used in a different verticals? For example: customer churn prediction with machine learning, evaluating customer lifetime value, sales forecasting.