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On Monday evening, Pajkatt won using an unusual item build (buying an early magic wand). Further training before Sumail's match on Thursday increased TrueSkill by two points. We set up the bot at a LAN event at The International, where players played over 1,000 games to beat the bot by any means possible. The game gave an obscure error message on GPU cloud instances.

Understanding overfitting: an inaccurate meme in supervised learning

It seems like, a kind of an urban legend or a meme, a folklore is circulating in data science or allied fields with the following statement: Applying cross-validation prevents overfitting and a good out-of-sample performance, low generalisation error in unseen data, indicates not an overfit. Aim In this post, we will give an intuition on why model validation as approximating generalization error of a model fit and detection of overfitting can not be resolved simultaneously on a single model. Let's use the following functional form, from classic text of Bishop, but with an added Gaussian noise $$f(x) sin(2\pi x) \mathcal{N}(0,0.1).$$ We generate large enough set, 100 points to avoid sample size issue discussed in Bishop's book, see Figure 2. Overtraining is not overfitting Overtraining means a model performance degrades in learning model parameters against an objective variable that effects how model is build, for example, an objective variable can be a training data size or iteration cycle in neural network.

How to Treat Missing Values in Your Data

Imputation of missing values from predictive techniques assumes that the nature of such missing observations are not observed completely at random and the variables chosen to impute such missing observations have some relationship with it, else it could yield imprecise estimates. There are various statistical methods like regression techniques, machine learning methods like SVM and/or data mining methods to impute such missing values. There are several predictive techniques; statistical and machine learning to impute missing values. Imputation of missing values is a tricky subject and unless the missing data is not observed completely at random, imputing such missing values by a Predictive Model is highly desirable since it can lead to better insights and overall increase in performance of your predictive models.

A simple experiment in Machine Learning Studio

In this machine learning tutorial, you'll follow five basic steps to build an experiment in Machine Learning Studio to create, train, and score your model: You can find a working copy of the following experiment in the Cortana Intelligence Gallery. Click the output port and select "Visualize" Datasets and modules have input and output ports represented by small circles - input ports at the top, output ports at the bottom. Type select columns in the Search box at the top of the module palette to find the Select Columns in Dataset module, then drag it to the experiment canvas. If you want to view the cleaned dataset, click the left output port of the Clean Missing Data module and select Visualize.

Decision Trees and Random Forests for Classification and Regression pt.1

We'll look at decision trees in this article and compare their classification performance using information derived from the Receiver Operating Characteristic (ROC) against logistic regression and a simple neural net. A Decision Tree is a tree (and a type of directed, acyclic graph) in which the nodes represent decisions (a square box), random transitions (a circular box) or terminal nodes, and the edges or branches are binary (yes/no, true/false) representing possible paths from one node to another. This also means that in principle, if we used only one feature in a predictive model, the proline content will allow us to predict correctly to a maximum 1-0.658 0.342 34.2% of the time, assuming that the original learned decision tree predicts perfectly. "Assuming that one is not interested in a specific trade-off between true positive rate and false positive rate (that is, a particular point on the ROC curve), the AUC [AUROC] is useful in that it aggregates performance across the entire range of trade-offs.

You don't need to be an expert to integrate AI in your startup

We've found that Amazon Machine Learning is a great place to start. AML differs from TensorFlow in a number of ways: with TensorFlow, you build your own models and can then execute them against your datasets wherever you like whereas AML requires you upload your dataset to Amazon then use their API to execute queries. The downside is you don't get to control the models and can't see into the workings of the system – you rely on Amazon to get it right. This "plug and play" type approach but is less customised and flexible, so you may end up needing replacing it with something more specialist in the future.

Multivariate Time Series Forecasting with LSTMs in Keras - Machine Learning Mastery

We will frame the supervised learning problem as predicting the pollution at the current hour (t) given the pollution measurement and weather conditions at the prior time step. We can see the 8 input variables (input series) and the 1 output variable (pollution level at the current hour). The example below splits the dataset into train and test sets, then splits the train and test sets into input and output variables. Running this example prints the shape of the train and test input and output sets with about 9K hours of data for training and about 35K hours for testing.

Deep Learning Course TensorFlow Course AI Training Edureka

Towards the end of the course, you will be working on a live project. We will emphasize on the concepts learned in the various modules through different case studies.

[D] Can I share a model trained on a non free dataset ? • r/MachineLearning

JPEG coefficients, latent vector of an autoencoder, whatever), but it's not clear that the coefficients of a learned transform become "tainted" by the copyrights of the materials used to learn the transform. Unless your autoencoder somehow encodes all of its inputs into a latent space such that, when you decode it back to the input space, it results in a copyrighted image (or something similar to a copyrighted image) different from what you provided as input (in which case, is it really an autoencoder?), the weights themselves will not be able to reconstruct copyrighted images without the appropriate latent vectors. Each single image on ImageNet can be copyrighted, but are global statistics of the ImageNet dataset copyrighted?