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Data
Data
Data

Data

Horusoftaceae can share and implement data science best practices to drive your decision-making with careful prognostication and effective root-cause analysis.

Data Science Services

Horusoftaceae has been applying data science in its various structures going from measurements to AI (counting its latest strategy | deep learning) to meet the most intentional investigation needs of our clients. We provide data science services as we see numerous enhancements that it can bring to organizations.

Data Science Solution Implementation

Do you consider building a data science solution for your organization? Horusoftaceae is prepared to actualize its prescribed procedures to guarantee an easily working data science solution that suits your business needs.

Data Science Improvement Consulting

On the off chance that you have experienced with an issue (boisterous or grimy data, off base forecasts, and so on) in your data science business, we can fill in as your research organization to assist you with making sense of how to change your data science.

Data Science Ongoing Consulting and Support

If you are looking for a nonstop support and evolution of your data science initiative, our team will closely cooperate with your subject matter experts and implementation team to provide ongoing recommendations and ensure the models’ continuous improvement.

Methods

To get to the significant experiences that your data covers up, we apply both statistical methods and expand AI calculations, including such mind-boggling methods as profound neural systems with 10+ shrouded layers.

Statistics methods

Statistics methods

Descriptive statistics
ARMA
ARIMA
Bayesian inference, etc.
Non-NN machine learning methods

Non-NN machine learning methods

Supervised learning algorithms, such as decision trees, linear regression, logistic regression, support vector machines.
Unsupervised learning algorithms, for example, K-means clustering and hierarchical clustering.
Reinforcement learning methods, such as Q-learning, SARSA, temporal differences method
Deep learning

Deep learning

Convolutional and recurrent neural networks (including LSTM and GRU)
Autoencoders
Generative adversarial networks (GANs)
Deep Q-network (DQN)
Bayesian deep learning

TECHNOLOGIES

Programming language

Programming language

Python
Java
Framework

Framework

Apache Mahout
Apache MXNet
Caffe
Torch
Libraries

Libraries

Azure ML Studio
Amazon Machine Learning