Examples of using Machine learning algorithms in English and their translations into Russian
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Practical Bayesian Optimization of Machine Learning Algorithms.
But like most machine learning algorithms, neural networks are stateless.
Examples include: pattern recognition, data mining, machine learning algorithms, and visualization.
Different machine learning algorithms may be used to accomplish this task.
The project database is to enable testing such methods of EEG data analysis as deep mining and machine learning algorithms.
The product employs machine learning algorithms in order to increase the efficiency of low-cost hardware.
Analysis of factors found will allow to select automatically individual recommendations for improving emotional state by using machine learning algorithms.
I was able to design and integrate machine learning algorithms for several large engineering companies.
Words sequences(n-grams), parts of speech, emoticons, features specific to particularresources are extracted and used as input for machine learning algorithms.
Now startups like Pomato are creating machine learning algorithms to automate resume screening processes.
Thanks to machine learning algorithms, the search engine and voice assistant Google understand English speech with the same accuracy as the native speakers.
Aisultan Shoiynbek, Development of tools for predictive analytics using machine learning algorithms in Big Data, Doctorate, Suleyman Demirel University.
But current machine learning algorithms aren't that good yet- they only work when focused a very specific, limited problem.
That's the core of our business, be it via A/B testing,personalization or our machine learning algorithms to identify the most promising visitor segments for your brand.
For such purposes we employ state-of-the-art computational methodologies(density functional treory, molecular dynamics, multiscale modeling) andcombine them with advanced machine learning algorithms.
Also, you can play around with tons of machine learning algorithms by downloading and installing SciKit-Learn.
Advanced machine learning algorithms would continuously juggle a thousand different parameters to ensure that power and cooling precisely mirror the ebb and flow of compute workloads.
An online lender, Upstart, analyze vast amounts of consumer data and utilizes machine learning algorithms to develop credit risk models that predict a consumer's likelihood of default.
In particular, machine learning algorithms in personalised subscriber communications and an omni-channel subscriber strategy increased the share of Big Data services in MegaFon's B2C revenue from 1.7% to 1.9% in 2016.
Mini-batch techniques are used with repeated passing over the training data to obtain optimized out-of-core versions of machine learning algorithms, for e.g. Stochastic gradient descent.
The company specialises in neural networks and machine learning algorithms that are capable of revealing and identifying objects, and facial expressions in real time.
The authors found that in addition to traditional computational experiments on a regular grid, computation using machine learning algorithms can become more productive to reseach Enhanced oil recovery EOR.
And this is just the beginning: our machine learning algorithms now make it possible for marketers to identify profitable visitor segments in real-time to make better decisions and get quick results.
With the accumulation of RNA-seq data that are capable of estimating expression profiles for alternatively spliced isoforms, machine learning algorithms have also been developed for predicting and differentiating functions at the isoform level.
They describe machine learning algorithms for inferring models of this type, and demonstrate its effectiveness at inferring course prerequisites from student enrollment data and at modeling web browser usage patterns.
It incorporated what was described as"MLX Technology", proprietary machine learning algorithms applied to the problem of accurately identifying spam email using 10,000 different attributes to differentiate between spam and valid email.
In practice, machine learning algorithms cope with that either by employing a convex approximation to the 0-1 loss function(like hinge loss for SVM), which is easier to optimize, or by imposing assumptions on the distribution P( x, y){\displaystyle P(x, y)} and thus stop being agnostic learning algorithms to which the above result applies.
You get a bunch of data,feed it into a machine learning algorithm, and then magically you have a world-class AI system running on your gaming laptop's video card….
Then we will train a machine learning algorithm to be able to find these 68 specific points on any face.
As CRF is supervised machine learning algorithm, you need to have large enough training sample to train it.