I'm conceptualizing a solver for a variant of sudoku called multi-sudoku, where multiple boards overlap like so:
If I understand the game correctly, you must solve each grid... moreI'm conceptualizing a solver for a variant of sudoku called multi-sudoku, where multiple boards overlap like so:
If I understand the game correctly, you must solve each grid in such a way that the overlap between any two or more grids is part of the solution for each.
I'm unsure as to how I should be thinking about this. Anybody got any hints/conceptual clues? Additionally, if any topics in artificial intelligence come to mind, I'd like to hear those too.
Out of curiosity, I've been reading up a bit on the field of Machine Learning, and I'm surprised at the amount of computation and mathematics involved. One book I'm reading... moreOut of curiosity, I've been reading up a bit on the field of Machine Learning, and I'm surprised at the amount of computation and mathematics involved. One book I'm reading through uses advanced concepts such as Ring Theory and PDEs (note: the only thing I know about PDEs is that they use that funny looking character). This strikes me as odd considering that mathematics itself is a hard thing to "learn."
Are there any branches of Machine Learning that use different approaches?
I would think that a approaches relying more on logic, memory, construction of unfounded assumptions, and over-generalizations would be a better way to go, since that seems more like the way animals think. Animals don't (explicitly) calculate probabilities and statistics; at least as far as I know. less
Coming from a programming background where you write code, test, deploy, run.. I'm trying to wrap my head around the concept of "training a model" or a "trained model" in data... moreComing from a programming background where you write code, test, deploy, run.. I'm trying to wrap my head around the concept of "training a model" or a "trained model" in data science, and deploying that trained model.
I'm not really concerned about the deployment environment, automation, etc.. I'm trying to understand the deployment unit.. a trained model. What does a trained model look like on a file system, what does it contain?
I understand the concept of training a model, and splitting a set of data into a training set and testing set, but lets say I have a notebook (python / jupyter) and I load in some data, split between training/testing data, and run an algorithm to "train" my model. What is my deliverable under the hood? While I'm training a model I'd think there'd be a certain amount of data being stored in memory.. so how does that become part of the trained model? It obviously can't contain all the data used for training; so for instance if I'm training a chatbot agent (retrieval-based),... less
My company has been using Jira for production issue tracking for last 6~8 years and as a result, there is a huge amount of production issue details logged in our Jira.
Usually... moreMy company has been using Jira for production issue tracking for last 6~8 years and as a result, there is a huge amount of production issue details logged in our Jira.
Usually each Jira ticket for any production support issues consist of some useful information such as:
Error Message
System Involved
Root Cause
Resolution
Time Taken
etc
My company has its own team chat service that supports the Chatbot API in Java / Python / etc. I would like to build the smart chatbot (if not AI) that is smart enough to exchange conversation like this in the chatroom:
DevOps) Hey Jirabot, what do you know about this error message?
Jirabot) Hi there, in which systems did this occur? Can you choose from one of the followings?
System A
System B
DevOps) 1
Jirabot) Right, it looks like following Jira tickets have experienced the similar issues.. please check the following tickets.
Jira-12zx
Jira-52123zz
Jira-vvvbbb
I would like to ask people with experiences in implementing something similar to this or have any... less
I'm aware of the gradient descent and the back-propagation algorithm. What I don't get is: when is using a bias important and how do you use it?
For example, when mapping... moreI'm aware of the gradient descent and the back-propagation algorithm. What I don't get is: when is using a bias important and how do you use it?
For example, when mapping the AND function, when I use 2 inputs and 1 output, it does not give the correct weights, however, when I use 3 inputs (1 of which is a bias), it gives the correct weights.
I am writing my own code for a decision tree. I need to decide on when to terminate the tree building process. I could think of limiting the height of the tree, but this seems... moreI am writing my own code for a decision tree. I need to decide on when to terminate the tree building process. I could think of limiting the height of the tree, but this seems trivial. Could anyone give me a better idea on how to implement my termination function.
Here in my tree building algorithm.
I want to implement machine learning on hardware platform s which can learning by itself Is there any way to by which machine learning on hardware works seamlessly?