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> 1) Machine learning may well deliver better results for questions you're already asking about data you already have, simply as an analytic or optimization tec
by ggregoire 8y ago
> 1) Machine learning may well deliver better results for questions you're already asking about data you already have, simply as an analytic or optimization technique. For example, our portfolio company Instacart built a system to optimize the routing of its personal shoppers through grocery stores that delivered a 50% improvement (this was built by just three engineers, using Google's open-source tools Keras and Tensorflow).
> 2) Machine learning lets you ask new questions of the data you already have. For example, a lawyer doing discovery might search for 'angry’ emails, or 'anxious’ or anomalous threads or clusters of documents, as well as doing keyword searches.
> 3) Third, machine learning opens up new data types to analysis - computers could not really read audio, images or video before and now, increasingly, that will be possible.
Are there any online resources to learn and implement step-by-step some ML to solve one of these use cases (or other ones)? Like a GitHub repo with data samples, a programming environment and then a step-by-step guide to solve trivial but real business problems?
- vishvananda 8y agoYou might find http://www.fast.ai/ http://www.fast.ai/ useful. Depending on your learning style, their courses can either be amazing or somewhat annoying. Their library includes jupyter notebooks so that you can work through the examples.
- anant90 8y agoCheck out Commonlounge: https://www.commonlounge.com/discussion/8d5612f5517944059a61f92aa8b86770 https://www.commonlounge.com/discussion/8d5612f5517944059a61... The courses have hands-on assignments that make you work through real-world problems.
- chasil 8y agoI am working through Google's machine learning crash course. https://developers.google.com/machine-learning/crash-course/ml-intro https://developers.google.com/machine-learning/crash-course/... Tensorflow sprays a lot of calculus. The idea is that all your known quantities ("features") become terms in an n-dimensional polynomial. The act of "training the model" is finding minima by traversing the negative gradient (the terms of the partial derivatives of any point when projected as a vector at that point). I'm glad I had calc 3, even if that was only 3-dimensional.
- fnbr 8y agoTechnically, it's not a polynomial as you can have nonlinear activation functions, such as the ReLU function. There's no polynomial that's equal to a network with ReLU activations (although, of course, a sufficiently large polynomial could come arbitrarily close). I would state that a neural network is a large, complicated, differentiable function, and the beauty of deep learning is that it turns out that by doing optimization that's derived from basic calculus, you can optimize this complicated function to do surprisingly useful things.
- skadamat 8y agoHey, I'm involved with a startup and we're aiming to just that -- provide a 0 to job-ready experience in machine learning. We're more of a paid, integrated experience than a repo with notebooks though. Here's our data science path - https://www.dataquest.io/path/data-scientist https://www.dataquest.io/path/data-scientist Every course has a project using real datasets - https://www.dataquest.io/projects https://www.dataquest.io/projects