Examples of 'deep learning algorithms' in a sentence
Meaning of "deep learning algorithms"
This phrase refers to a set of advanced computational algorithms that are designed to mimic the human brain's ability to learn, adapt, and make decisions based on patterns and data. It suggests the utilization of complex mathematical models to process large amounts of information and derive meaningful insights or predictions
How to use "deep learning algorithms" in a sentence
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deep learning algorithms
Deep learning algorithms are based on distributed representations.
Artificial intelligence and deep learning algorithms.
Deep learning algorithms are stacked in a hierarchy of increasing abstraction.
Evaluation is carried out by powerful deep learning algorithms.
Deep learning algorithms are stacked in a hierarchy of increasing complexity.
Are now understandable with deep learning algorithms.
Deep learning algorithms can be applied to unsupervised learning tasks.
Automatic generation of prescriptive reports through deep learning algorithms.
Most deep learning algorithms operate like black boxes.
All of them use artificial intelligence and deep learning algorithms.
These deep learning algorithms review data and arrive at conclusions.
These feedback connections are not present in most deep learning algorithms.
Deep learning algorithms analyze the ergonomics and efficiency of our processes.
Scientists want bacteria to be identified by their appearance thanks to deep learning algorithms.
Deep learning algorithms are a new set of powerful methods for machine learning.
See also
The basis of everything is in machine learning and in particular in deep learning algorithms.
But other deep learning algorithms of ours are also now available to billions of users.
We first study the problem of reducing the computational cost of some deep learning algorithms.
Deep learning algorithms are used to further interpret the results generated by these libraries.
Its presence has supported the rapid development of object classification deep learning algorithms.
New deep learning algorithms can detect unexpected situations and hidden potential.
It can even bring great results thanks to AI and deep learning algorithms.
The opaque nature of deep learning algorithms is also an issue for debate and regulation.
Development and implementation of Deep Learning algorithms.
Deep learning algorithms transform their inputs through more layers than shallow learning algorithms.
What 's new are the deep learning algorithms.
Deep learning algorithms deal with repetition and combinatorial situations rather than with complexity.
Python is a high-level interpreted programming language in which deep learning algorithms are implemented.
The machines rely on deep learning algorithms to spot their target and avoid harming plants.
Complex, nuanced sentences like this one are now understandable with deep learning algorithms.
Deep Learning algorithms extract required data.
Today it's possible to create real-world applications with machine learning and deep learning algorithms.
The Deep Learning algorithms learn with predefined pairs of inputs and outputs.
For this, Kuri uses vision and deep learning algorithms.
Deep learning algorithms are dependent on data, lots of it.
LSTMs ( long short-term memory networks ) are neural networks used by deep learning algorithms.
Deep learning algorithms do complicated things, like matrix multiplications.
Caffe supports Open Multi-Processing ( OpenMP ) for parallelizing deep learning algorithms over a cluster of systems.
In particular, deep learning algorithms achieves the most accurate results.
Deep learning algorithms parse data to make informed decisions, serving as the basis of automation.
Additionally, deep learning algorithms can identify relationships that traditional machine learning algorithms may miss.
Unlike NLP, Deep Learning algorithms do not exclusively deal with text.
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