All this money and attention will surely pump up the R stock/s like crazy. Not sure if these investors are also receiving any preferntial liquidation preference too. Is it not likely that they might dump stock to book huge profit and the retail Indian investors will be left holding the bag ?
Right! I was hoping to see the body of one of their system induced algorithms. They don't seem to have included any - or maybe I am mistaken and their system is opaque and does not allow a generated algo. to be read out...
Good starter project to flex your JS muscle. But other than that - Zoom's built-in whiteboard is pretty feature-rich and meets my use-cases perfectly. My use-cases -: brainstorming with colleagues, interviews.
Will things like WP (not in particular) die out eventually because of poor developer experience ?
Corollary: frameworks liked by developers will eventually stick even if currently they have a modest userbase.
I liked the boldness of this idea.
But 'something' needs to select the sklearn model, tune its hyper-params - how long can you keep it all hidden away from the user?
The training phase can be considerably long. Have you thought of some kind of an async wrapper that Smart Fruit might provide or will the user be expected to code it up?
This is more of a user experience comment - when the interface is designed to feel as if one is interacting with a DB / ORM the user may come to assume that the outcomes will be deterministic... While the returned results will remain deterministic given the training data, model and hyper-parameters remain the same - it won't feel as deterministic when either of these is updated... I am not sure if I communicated my concern clearly. I am trying to understand who the intended end-user is, of this package...
A logistic regression model is typically used for a classification task. 'Fitting a logistic model' entails finding optimal coefficients / 'weights' of input features such that classification error is minimised.
For a binary classification task, one could simply calculate mean squared error between predicted values and actual labels (as in linear regression) and then proceed to find the optimal weights iteratively using gradient descent. But the sigmoid shape of the logistic function makes gradient descent a poor choice of an optimization technique (w.r.t. lack of guarantee of finding a global optimum).
A surer way to find globally optimal weights is using the Newton's method of calculating weight updates. This is a numerical optimization technique that requires one to calculate the 1st and 2nd order derivatives of the error function. The matrix that 'calculates' the 1st order derivative is called a Jacobian and the one that calculates the 2nd order derivative is called a Hessian...
Yet another tech that nobody wants. They are the next worst thing after the "allow notifications" pop-up that every landing page seems to be infested with these days.
> Also it got better than humans in those games from scratch in about 4 hours whereas humans have had 2000 years to study them so you can forgive it some resource usage.
Few would care. Your examiner doesn't give you extra marks on a given problem for finishing your homework quickly.
Questions: - what is the measurement precision?
- do you need calibration? How do you do it in production?
- what it is the root problem you are trying to solve?
- what is your hypothesis about your solution- quantitatively?