Deep Research and Development

Ok, so you've launched your cool NFT game, that you'd been working on it for years. The platform is ready, the tokens are minted, the users are hyped and coming in droves. Sustainable scaling the product is not easy - I mean just listen to Tony Robbins . And while the token economics have be drafted in the whitepaper, the users would have to actually behave the similarly to what you'd invisioned. For instance, suppose you have an RPG , where user acquire experience and level up. In order to level up, they need to earn in-game artifacts, which can also be bought in the marketplace (where you charge transaction fees - here comes your business model). After having some thought you have drafted the approximate path of users' level progress and even ran Monte Carlo simulation to get some sence of possible distribution. Having finalised all the statistical models and simulations, you've come to the following conclusion - an average user will upgrade the free ban...

Introduction This article continues the Python for Data Scientists series by talking about SciPy . It is built on top of NumPy, of which we've already talked in the previous article . SciPy provides many user-friendly and efficient numerical routines addressing a number of different standard problem domains in scientific computing such as integration, differential and sparse linear system solvers, optimizers and root finding algorithms, Fourier Transforms, various standard continuous and discrete probability distributions and many more. Together NumPy and SciPy form a reasonably complete computational replacement for much of MATLAB along with some of its add-on toolboxes. Installation Installation of SciPy is trivial. In many cases, it will be already supplied to you with python distribution, or as usual may be installed manually using python package manager pip install scipy Depending on the running OS, you might be needing to install gfortran ...