Examples of using Learning algorithms in English and their translations into Hindi
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Advanced Machine Learning Algorithms.
Machine learning algorithms are often categorized as.
We then go into learning algorithms.
Machine learning algorithms are often divided into some categories.
The second threat comes from the application of machine learning algorithms to“big data.”.
Machine learning algorithms can also sort through millions of posts much more quickly than any human.
It is really difficult to get enough data for reinforcement learning algorithms.
Our software leverages machine learning algorithms, that enable automatic translation and autocorrection.
This core ECE course covers digital control systems, Markov decision processes,and reinforcement learning algorithms.
Manifold learning algorithms attempt to do so under the constraint that the learned  representation is low-dimensional.
Software suites containing a variety of machine learning algorithms include the following.
Using machine learning algorithms, we were able to forecast whether a person was going to troll about 80 percent of the time.
So this can be classified asanonymised data used for training machine learning algorithms, but of very limited use otherwise.
Di Minin is creating machine learning algorithms capable of identifying posts on social media that are related to illegal wildlife trade.
The world we live in leaves a digital footprint in almost all operations andthat serves as a stepping stone for machine learning algorithms.
Artificial intelligence(AI) and machine learning algorithms are transforming systems, experiences, processes, and entire industries.
The world where we live leaves a digital footprint in almost all activities,and that serves as the stepping stone for machine learning algorithms.
But as machine learning algorithms improve, robots will respond to their environments in ways that humans didn't explicitly teach them to.
The course teaches statistics for business analysis, machine learning algorithms, deep learning  with TensorFlow, and programming with Python.
AI and machine learning algorithms are routinely used to predict our purchasing behavior and to recognize our faces or handwriting.
So far we have evaluated our methods on decision systems that we created by training common machine learning algorithms with real world data sets.
The cloud is important because our learning algorithms perform best when they look at large data sets to find patterns and create their models.
Data scientists say that computer programs, neural networks,machine learning algorithms and artificial intelligence(AI) work because they learn  how….
Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of(or generating) lower-level features.
Although great promise has been shown with deep learning algorithms in a variety of tasks across radiology and medicine, these systems are far from perfect.
Multilinear subspace learning algorithms aim to learn  low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into(high-dimensional) vectors.
Right now, companies are developing machine learning algorithms to recognize emotions, but they only train their models in cropped faces and based on facial expression.
Mathematical analysis of machine learning algorithms and their performance is a distinct category of theoretical computer science often mentioned as computational learning  theory.
Over the last few years, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer.