# Tag Info

## Hot answers tagged ai

10

The subject areas Artifical Intelligence and Machine Learning (plus Data Science) are loosely defined, such that it is hard to make strict statements about how they relate. In the general case, it seems that there are parts that overlap, but that they are quite far from being "the same subject with two different names" as suggested in the question. The term ...

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Machine learning in layman terms is an algorithm that allows machines to identify patterns in data and then develop a model which can be used to predict unseen data. Artificial Intelligence is the ability of machines to make intelligent decisions which are equal to or better than their human counterpart. Difference between the two: A.I. is a very broad ...

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The two books that come into my mind are: Artificial Intelligence: A Modern Approach The Deep Learning Book They both start from the basics and escalate while moving on. Also thanks for your recommendation, I'll take a look at it because I want to jump to finance at some point in my career:)

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Though neither are well defined, as commonly used they are somewhat orthogonal concepts. In my opinion, AI has a fairly narrow definition - it is about optimization through actions. AI is about decision making, either in deterministic or probabilistic environments. Typically, this is operationalized as action selection to maximize some reward function, or ...

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Deep Learning is a subset of Machine Learning which is a subset of Artificial Intelligence. Machine learning is a particular approach for AI but not the only one. Symbolic Logic, Bayersian Statistics are a few examples of AI approaches which do not use any kind of machine learning algorithms.

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Author of the paper here - I missed that this is apparently not a TensorFlow function, it's equivalent to Sonnet's scale_gradient, or the following function: def scale_gradient(tensor, scale): """Scales the gradient for the backward pass.""" return tensor * scale + tf.stop_gradient(tensor) * (1 - scale)

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I would not concern yourself too much about any structuring of knowledge that declares that one subject is categorised as one thing or another. These structures are often wrong and knowledge in general is more fluid and difficult to define than can be viewed as some kind of Venn diagram. In addition, both Data Science and AI are poorly defined, and have ...

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What do you want to learn in AI and Machine learning? Artificial Intelligence covers many practical applications, so your question might be a bit vague here. I will suggest you books on Machine learning itself, as it is as a part of Artificial Intelligence. Simply stated the goal of Machine learning is two-fold: inference and prediction. Inference: the ...

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Yes, that is possible. It can be done in the following way: We assume that the action distribution is guassian, i.e. that we need to learn the parameters $\theta$ of $\mathcal{N}(a|\mu_\theta,\sigma_\theta)$. Let's say that $\theta$ is given by the weights of a neural network, which we find by optimizing the objective \max_\theta \mathbb{E}_{p_{\theta}}\...

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First of all, you have to split your data set into train/test splits before doing any over/under sampling. If you do any strategy based on your approaches, and then split data you will bias your model and that is wrong simply because you are introducing points on your future test set that does not exist and your scores estimations would be imperfect. After ...

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ML, by Tom M. Mitchell: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. AI, but not ML: SLAM Path finding: Bellman–Ford algorithm, A* search algorithm, Dijkstra's algorithm Markov chains cellular automata ...

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A good example of AI, but not machine learning is evolutionary computation. Here instead of learning from experience (as in Tom M. Mitchell's definition) we have genotype changing in each generation of computer program version, measured by its performance at task (phenotype expression in environment). As Melanie Mitchell puts it: '...from the earliest days ...

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If I understood correctly, you will eventually have, after some time, those user ratings right? So, assuming that you will have some labeled data (i.e. user ratings together with the features you say) to train with, you can build a multivariate regression model (you can have a first look at linear models to begin with). This approach is similar to what you ...

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As the great Tom Mitchell has said in his book "Machine Learning is the ability to learn without being explicitly programmed." Machine learning algorithms are widely employed and are encountered daily. Examples are automatic recommendations when buying a product or voice recognition software that adapts to your voice. AI is any technology that enables a ...

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Let's take the total Turing test as an example. A computer is often said to be intelligent if it can pass the total Turing test. A computer passes the test if a human interrogator, after posing some written questions, cannot tell whether the written responses come from a person or from a computer. The total Turing Test also includes a video signal so that ...

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One of the problems that can occur when training a neural network is known as the exploding gradient problem. A poorly initialised network could lead to a large increase in the norm of the gradient during training. These larger values will basically run the weights out of the number precision of the computer, resulting in NaN values. This post gives more ...

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AI and Machine Learning is a big field. If you want the broadest nontrivial introduction, you should check out: Machine Learning: A Probabilistic Perspective. It covers everything from classical statistical methods to graphical models and deep learning. If you are specifically interested in topics having more to do with AI than machine learning, I think ...

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If I train an agent for taking actions for 15 mins during the training process, is it ok I make my agent take actions at every 5 min interval during deployment? It is impossible to say in general. Depending on the nature of the environment and controller, this may work just fine, or may completely destroy the ability of the agent to function at all. I ...

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I highly doubt that the job itself will going to be at risk. It is rather the other way around: Data science and machine learning will replace a lot of other jobs. In the end there, at least, always needs to be someone providing the data to the machine. It seems like your question rather should be "will there be enough data science positions in the future ...

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It actually depends on the role you get as a Data Scientist. If you have to write production-quality code at a large software company, then you need to be knowing the basics of Object-Oriented Programming (OOP). Object-Oriented Design (OOD), however, is something you need not necessarily know in a Data Science role. Learning OOD in case you plan a switch to ...

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Using python vs R is more of a personal choice, but most people I know in data science, including myself, use python. If you decide to take up python, almost all of the python ML libraries are written using the OOP approach, hence, some of the API design and interactions (such as error/warning messages) will make more sense if you are familiar with basic OOP ...

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Your confusion seems to come from mixing up between some policy $\pi$ and an optimal policy $\pi^*$. Your summary is generally correct, but missing these extra details. Let me try go through it again. Starting with the MDP definitions: First of all, we have the transition probabilities $T(s,a,s') = P(s'|s,a)$ which are conditional probabilities of arriving ...

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One-Shot Learning refers to the problem when you only have very few or a single sample for some classes in your training dataset. A common application is, for example, face recognition. Here you may have only a single image per person in your dataset. Nevertheless, you'd like your neural net to be able to recognize that person from new images. A good intro ...

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There are a couple of things I would suggest: Reshape the input data: It looks to me that you want to analyse a time series if IQ-values and each time series is 128 datapoints. In this case you probably want to treat I and Q as the channels respectively and convolve over ther 128 points. To do this the input data needs to be of shape (128, 2). Right now you ...

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There is relatively little data for a deep learning solution - 220 total data points and 20 data points for each of the 11 labels. Increasing the amount of data would probably have the greatest impact on model performance. The best option would be to collect more data. Another option would be data augmentation.

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Since your model seems to be doing fine on the correct hand signs, I don't think there is anything wrong. It's a common misconception that you can interpret the model output as a confidence measure. Consider the following example. Let's say you have a binary classifer and you learn to distinguish numbers between -1 and 0 in one class labelled as 0 and ...

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Given that its pseude code? (since its not in TF 2.0) I would go with gradient clipping or batch normalisation ('scaling of activation functions')

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Is my understanding correct and is it recommended to have such a reward structure for my use case ? Your understanding is not correct, and setting extremely high rewards for the goal state in this case can backfire. Probably the most important way it could backfire in your case, is that your scaling of bad results becomes irrelevant. The difference between ...

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TL;DR: Relative scale of multiple different rewards can be important. However, granting +10 for a win and -1 for a loss in a game will not improve speed of learning how to win any better than tuning the learning rate. from a given state if a agent takes a good action i give a positive reward, and if the action is bad, i give a negative reward. Usually ...

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Yes it is possible. You can train end to end an RNN over the training data. It means input the user query as an input and set the output for the sql query. Definitely the most challenging part is preparing the training set as RNN needs a lot of data to be learned. Also, for the training set, it might you can use a GAN‌ to generate more queries for the ...

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