# Tag Info

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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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Interpreted languages allow for a faster development cycle, as they don't require time for compilation, and fragments can often be run without having a complete program. They often also have fewer constraints for variable declaration or typing. That means they can be used to quickly scope out a problem and try different solutions. The drawback is the slower ...

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While dyedgreen is right in some respects, I don't agree entirely with that sentiment. Sure, you can theoretically use any language as long as you know the maths and understand the concepts inside and out whilst having some applicable knowledge. However, I don't believe if you are starting from scratch, you should learn to develop models in Java. While the ...

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I don’t know for certain, but I can make a guess. This is just my opinion, some others may disagree. The field of ALife has four branches that I’m aware of: Self-Organizing/self assembly behavior. This is the application you refer to, another context it’s useful is swarm control (for drone swarms, for example). While this is technically ALife, as far as I’m ...

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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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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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TensorFlow was developed by Google and is based on Theano (Python library), while Facebook developed PyTorch using the Torch library. Both frames are useful and have a great community behind them. Both provide machine learning libraries to accomplish various tasks and do the job. TensorFlow is a powerful and deep learning tool with active visualization and ...

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I don't think there is much merit in using one language over the other. It's true that you will get a better feeling for what libraries etc. are available in Python when using it, but I think it's more important to focus on the mathematics and application of the algorithms and techniques you will learn, as these will be relevant regardless of the language / ...

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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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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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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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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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I'm going to go ahead and disagree with the others. From an academic perspective for AI or any CS related assignment Java (or C or C++) will always have much more benefit as you will get to write the actual code instead of using libraries others have already written. That way later on when you transition to Python or whatever language you choose you'll ...

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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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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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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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This partly answer to question 1. There is no general rule concerning accuracy or size of the model. It depends on the training data and the processed data. The lightest is your model compared to the full accuracy model the less accurate it will be. I would run the lite model on test data and compare to the accuracy of the full model to get an exact measure ...

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Q-values represent expected return after taking action $a$ in state $s$, so they do tell you how good it is to take an action in the specific state. Better actions will have larger Q-values. Q-values can be used to compares actions but they are not very meaningful in representing performance of the agent since you have nothing to compare them with. You don't ...

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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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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 ...

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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 ...

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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 ...

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Assembling groups of messages with the objective of assembling conversations from a mix of them has process features in common with reconstituting a drive with overwritten indexing information, reassembling broken items that have been intermixed, or transcribing music with appropriate assignments of notes to instruments in the staff based on frequency ...

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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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