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Keras is a simple and high-level neural networks library, written in Python, that works as a wrapper for Tensorflow and Theano. It's easy to learn and use. Using Keras is like working with Lego blocks. It was built so that people can do quick experiments and proofs-of-concept before launching into a full-scale build process. With that in mind, it was made ...


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The "Turing Test" is generally taken to mean an updated version of the Imitation Game Alan Turing proposed in his 1951 paper of the same name. An early version had a human (male or female) and a computer, and a judge had to decide which is which, and what gender they were if human. If they were correct less than 50% then the computer was considered "...


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If you want to do machine learning without coding (eg visual programming) you can try : Orange : https://orange.biolab.si/ . It also come when you download the Anaconda distribution. Also there is Dataiku Data Science Studio (the free version). https://www.dataiku.com/dss/trynow/ But in general I would not recommend doing machine learning / data science ...


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In recent times different data science magazines and institutions have published their reviews of the top AI toolkits. In these reviews they tend to highlight the innovative features possessed by each platform as well as their reliability and ability to scale. Below are a some evaluations of AI platforms that I recommend you have a look at: KDnuggets ...


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There are really two questions here, that I can see. One is "what were the specific requirements of the original Turing test, as stated by Turing himself?" The other is "What should the specific requirements of a modern Turing test be?" Things have advanced a lot since Turing's day, and I think it's reasonable for us to consider extending/modifying his test ...


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The Intel Movidius Stick is an USB stick for running a neural networks (that are programmed in Tensor Flow) on resource scare devices such as a Raspberry Pi (in order to reduce the traffic by sending data to cloud service or a server with more capacity where your neural network runs). Tensor Flow is a programming framework which can be used as python ...


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I would advise you to look into Mozilla’s implementation of Baidu DeepSpeech here


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According to what Josh Dotson posted via medium,gives a clear insightful knowledge concerning the following; 1.Speech data besides speech recognition. Language modelling. Text to speech. Machine translation. Signal processing. And lastly, books and blogs for further research Resources for acknowledgement


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I don't know about voice recognition but for NLP i think that Gensim could be what you are looking for! Gensim is a NLP package that contains efficient implementations of many well known functionalities for the tasks of topic modeling such as tf–idf, Latent Dirichlet allocation, Latent semantic analysis... About the readings, maybe you can start with the ...


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Since you are A C# developer already Just getting started and not sure where to go next I would suggest trying the Emotion API which is now part of the general Face API. This has the benefit of being pre-trained on a very large dataset. You can perform 30,000 recognitions/month for free.


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You could try Mesa. It has various examples that are commonly-used in agent-based modelling, like Epstein's model, a wolf/sheep predator/prey model, and many more. There is also an introductory tutorial.


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Visual methods are limited and you will soon find the time invested in coding may have been better. There are various softwares like Weka [website] or Rapid Miner (Not free) [website] or Orange [website]. Nice thing is they provide GUI as well as coding. So you can import Python or R script in rapid miner for example. But I would recommend that you should ...


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TLDR Use whatever environment you are most comfortable with. Today the choice of operating system is of much less importance than few years ago. Generally speaking, you will have more application choices under Linux, however, some of these choices will be less than spectacular :-) Windows works better with some hardware. Hence, under Linux, it is a very ...


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When you want to compare Reinforcement Learning algorithms, you might want to compare the average rewards they generate and how fast and close they get to the optimal policy. However, in the case of comparing it to humans, you might want to compare the game results of all the games played. Reward Comparison Often Reinforcement Learning algorithms are ...


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You might want to look into building convolutional neural network (CNN) for object detection using Keras. With plain white squares, it should work pretty good.


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As @Clement mentions, text_gen_description gives a good overview!, but the paper seqGAN paper describes the REINFORCE approach more in depth, as they are the first to do it (i believe). This is probably the approach most take now of days when going the GAN route. Note that just basic MLE training has shown promise with openAI's GPT2. When i need a text ...


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Welcome to AI.SE Enes. I think by random search, you are referring to so-called "black-box optimization". Random search is sometimes used as a name for this, but BBO is a more common name, and might be easier to search for. There are many BBO techniques. 'random search' is usually used to refer to a hill-climbing algorithm where you start at a random ...


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I can't do a better job at explaining than Jeremy Howard's team did. Follow carefully and systematically all the steps put together here. You can read more about the different types of machines here, about the possible choices of GPUs here and about the regions and zones here. Follow the fastai course too. I can't emphasize how good is it. You can find ...


1

Android provides functionality for running inferences over TensorFlow models. TensorFlow Lite is an extention of TensorFlow which can run TF models on Android as well as iOS. For Android, we have various Java dependencies which could get TF Lite directly in code. TensorFlow, itself is in C++ and is used in Python, but TensorFlowJS is a machine learning ...


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Addressing your how-to-get-started point.... A currently popular approach to creating intelligent software is to use machine learning. The most popular language for implementing a machine learning system is Python. Take a look at https://scikit-learn.org/stable/ as an example of a machine learning Python framework. It does help to know what you are doing ...


1

Since you require a voice-operated system, the first thing you will need is a speech-to-text converter. A good speech-to-text engine would be able to recognize words properly and thereby increasing the user-friendliness. A normal person could not create or train it. These engines are provided by Google Cloud. Next thing, is an intelligent system which would ...


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This is a tricky issue. I assume you are using transition probabilities to pick the next suitable word, so you could use some other corpus data, derive probabilities from it, and compare those to your system. Not very satisfactory, though, as you might end up evaluating the system in a circular way, deriving your test data in the same way as you generate ...


1

For a midsize corporation running multiple cafeterias, an AI tool may be feasible, provided sufficient time and resources are invested well in advance of system use. Selling as a full strategy an AI tool without a corporate commitment to a strategy which includes costs listed below is unwise. As of this writing and for the foreseeable future, there are no ...


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In the field of Automatic Speech Recognition (ASR) Kaldi is the current leader. Before Deep Neural Network era there were Sphinx and HTK.


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I suggest you to go through the r-cnn paper or go through a tutorial on it . CNNs transform the image into high dimensional vector in their last layer , in case of classification this vector is sent to a "softmax" layer , in case of bounding box regression , four values :length , breadth , location of one of the points of the bounding box , are regressed ...


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This is supposed to be a comment but I haven't got enough reputation to do that. In addition to what @the complexitytheorist has said, I recommend you to have a deeper look at your data first, using dimension reduction and visualisation methods such as PCA and t-SNE. A better understanding of data may always save you a lot of work. Then you can choose ...


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I would recommend to have a look at Finding Groups in Data, which is a very readable introduction to clustering methods. It gives a good overview over a number of different algorithms, both agglomerative and hierarchical. As far as I remember, source code for the various algorithms is available on the web somewhere. I am sure you will find a fitting ...


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In recent years the focus has been on layers rather than the more biologically inspired individual nodes. As stated in the comment by thecomplexitytheorist you could use a computational graph, although then you have issues with distribution and you're limited to one framework. I created something in my PhD about the same time as the thesis you reference ...


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Knime is my favorite - it is the Gartner Leader Quadrant - available for free. Also consider Rattle (for R) and Gretl for econometrics/stats. These are good tools for the Citizen Data Scientist.


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The difference between a Speech API and a Speech Engine is: Speech API's enable developers to integrate speech recognition technologies into developer apps. On the other hand a speech engine is software that gives your computer the ability to play back text in a spoken voice. (Source msdn library) Below is a list of speech recognition tool-kits and their ...


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