About us

Deep Solutions delivers end to end software solutions based on deep learning innovative algorithms for computer vision, natural language processing, anomaly detection, recommendation systems and more.

Our Vision

Reaching a period where software development is very efficient and optimized, we introduced intelligence in our machines via machine learning paradigms to make elegant software that solves real world problems by utilizing data.

Now, looking to go beyond shallow learning to model complex networks,an emerging need for deep learning is required. We believe deep learning would not solve all world's software problems, though, it will certainly and eventually solve many of them.

We want to take part in that venture.

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Our Services

Provide your company with state of the art solutions.
Consultation. Implementation. Research.

Image recognition

Natural language understanding

Object detection

Time series prediction

Anomaly Detection

Recommendation systems

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Our Blog

Capturing semantic meanings using deep learning

Capturing semantic meanings using deep learning

Word embedding is a technique that treats words as vectors whose relative similarities correlate with semantic similarity. This technique is one of the most successful applications of unsupervised learning. Natural language processing (NLP)systems traditionally encode words as strings, which are arbitrary and provide no useful information to the system regarding the relationships that may exist between different words. Word embedding is an alternative technique in NLP whereby words or phrases from the vocabulary are mapped to vectors of real numbers in a low-dimensional space relative to the vocabulary size, and the similarities between the vectors correlate with the words’ semantic similarity.Continue Reading

The Power of Data Augmentation

The Power of Data Augmentation

A review of the timing of the most publicized AI advances suggests that perhaps many major AI breakthroughs have actually been constrained by the availability of high-quality training data sets, and not by algorithmic advances.
The preference of high-quality training data sets over purely algorithmic advances might allow an order-of-magnitude speedup in AI breakthroughs.
However, getting this data is neither an easy nor a cheap task, Mechanical Turk tagging data-sets campaigns could cost hundreds of dollars easily and yet with an uncertain quality.
Therefore, the question is how to exploit the minimal data we have and still be able to learn well.

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A Fast CNN Face Detection

A Fast CNN Face Detection

Face detection, a sub problem of object detection , is a challenging task due to the fact that faces may appear in various pose, scale, facial expression, occlusion, and lighting settings. One of the simplest and common ways to perform an object detection in the world of deep neural networks is a sliding window approach where an image is scanned sequentially, by running a network on it, inspecting each fixed size window at a time. Unfortunately, despite giving (if trained correctly) promising accuracy ,this routine is costly and therefore greatly degenerates a given detector speed.A better solution would be to transform a given network into a fully-convolutional network by converting fully-connected layers to convolution layers. The reason therefore lay in the fact that convolutional outputs are shared between multiple windows.

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