artificial-intelligence

New to machine learning so looking for some direction how to get started. The end goal is to be able to train a model to count the number of objects in an image using Tensorflow. My initial focus will be to train the model to count one specific type of object. So lets say I take coins. I will only train the model to count coins. Not worried about creating a generic counter for all different types of objects. I've only done Google's example of image classification of flowers and I understand the basics of that. So looking for clues how to get started. Is this an image classification problem and

I have a simple question regarding the Minimax algorithm: for example for the tic-tac-toe game, how do I determine the utility function's for each player plays? It doesn't do that automatically, does it? I must hard-code the values in the game, it can't learn them by itself, does it? No, a MiniMax does not learn. It is a smarter version of a brute-force tree search. Typically you would implement the utility function directly. In this case the algorithm would not learn how to play the game, it would use the information that you had explicitly hard-coded in the implementation. However, it would

I am looking to implement a generic Neural Net, with 1 Input Layer consisting of Input Nodes, 1 Output Layer consisting of Output Nodes, and N Hidden Layers consisting of Hidden Nodes. Nodes are organized into Layers, with the rule that Nodes in the same Layer cannot be connected. I mostly understand the concept of the Bias, and my question is this: Should there be one Bias value per Layer (shared by all nodes in that Layer) or should each Node (except Nodes in the Input Layer) have their own Bias value? I have a feeling it could be done both ways, and would like to understand the trade offs

Does anyone know a good algorithm for perform clustering on both discrete and continuous attributes? I am working on a problem of identifying a group of similar customers and each customer has both discrete and continuous attributes (Think type of customers, amount of revenue generated by this customer, geographic location and etc..) Traditionally algorithm like K-means or EM work for continuous attributes, what if we have a mix of continuous and discrete attributes? If I remember correctly, then COBWEB algorithm could work with discrete attributes. And you can also do different 'tricks' to

A little background: as a way to learn multinode trees in C++, I decided to generate all possible TicTacToe boards and store them in a tree such that the branch beginning at a node are all boards that can follow from that node, and the children of a node are boards that follow in one move. After that, I thought it would be fun to write an AI to play TicTacToe using that tree as a decision tree. TTT is a solvable problem where a perfect player will never lose, so it seemed an easy AI to code for my first time trying an AI. Now when I first implemented the AI, I went back and added two fields to