artificial-intelligence

问题 I'm a new-bee to AI and want to perform the below exercise. Can you please suggest the way to achieve it using python: Scenario - I have list of businesses of some companies as below like: 1. AI 2. Artificial Intelligence 3. VR 4. Virtual reality 5. Mobile application 6. Desktop softwares and want to categorize them as below: Technology ---> Category 1. AI ---> Category Artificial Intelligence 2. Artificial Intelligence ---> Category Artificial Intelligence 3. VR ---> Category Virtual Reality

问题 It's difficult to tell what is being asked here. This question is ambiguous, vague, incomplete, overly broad, or rhetorical and cannot be reasonably answered in its current form. For help clarifying this question so that it can be reopened, visit the help center. Closed 7 years ago . I'm developing a game(Tank Game 2D),(eg - link ) AI player. My player will be one of 5 other players(AI also) who playing for obtaining maximum coins appears randomly somewhere in the grid.(take a look at the

问题 I have 3 Questions about fine-tuning word vectors. Please, help me out. I will really appreciate it! Many thanks in advance! When I train my own CNN for text classification, I use Word2vec to initialize the words, then I just employ these pre-trained vectors as my input features to train CNN, so if I never had a embedding layer, it surely can not do any fine-tunes through back-propagation. my question is if I want to do fine-tuning, does it means to create a Embedding layer?and how to create

问题 I have followed an article on Speech Recognition with Delphi (SAPI 5.3). http://edn.embarcadero.com/article/29583 I created a basic application. but the problem is that the application has got it all wrong ! it doesn't get what I am saying correctly. if i say for example : "word", it get "ward". and so on. is there any better way to do speech recognition anyone can give me ? 回答1: For any speech recognition tool, you do have to make sure you give it enough training so it can recognize the way

问题 I have a dictionary created from the DictionaryCreator from UIMA, I would like to annotate a piece of text using the DictionaryAnnotator and the aforementioned dictionary, I could not figure out how to get the annotated text. Please let me know if you do. Any help is appreciated. The code, the dictionary-file and the descriptor is mentioned below, P.S. I'm new to Apache UIMA. XMLInputSource xml_in = new XMLInputSource("DictionaryAnnotatorDescriptor.xml"); ResourceSpecifier specifier =

问题 I have a sentence in English. Now I want to jumble the words up and input that set of words into a program which should unscramble the words according to normal rules of English grammar to output the original sentence. I can vaguely assume it would require Natural Language Generation algorithms. For eg: Sentence: Mary has gone for a walk with her dog. Set of words: {has, for, a, with, her, dog, Mary, gone, walk} The output should be the same sentence. I can assume only the set of words will

问题 I'm new to Neural Networks, and programming generally. I've written a neural network in java, and i'm looking at football data. I have two inputs: 1) Home team win % over n games 2) Away team win % over n games Using 'standard statistical models' one can predict the number of goals that will occur in a match using these two numbers alone, with a reasonable degree of accuracy. However, when i attempt to train my NN to predict the number of goals, it simply doesn't converge :( I'm using a

问题 I am putting together a project for a class that requires me to put AI in a top down Tactical Strategy game in Flash AS3. I decided that I would use a node based path finding approach because the game is based on a circular movement scheme. When a player moves a unit he essentially draws a series of line segments that connect that a player unit will follow along. I am trying to put together a similar operation for the AI units in our game by creating a list of nodes to traverse to a target

问题 I've read (for example, http://radagast.se/othello/Help/order.html) that searching on the best moves at each level first (which can be found using iterative deepening) makes the search go much faster. How would one go about searching on the best moves possible without using too much additional memory and cpu time? 回答1: There are basically two strategies: Static move ordering Dynamic move ordering Dynamic move ordering uses information from previous searches, either because you transpose into

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