Training
Enhanced a deep reinforcement learning model?s performance and exploratory capabilities by introducing stochasticity onto deep neural networks using PyTorch on JetBrains PyCharm.
Conducted novel research in the domain of deep reinforcement learning for optimized decision-making in uncertain environments.
Explored the symbiosis between neural networks and stochastic elements such as Bayesian neural networks, enhancing adaptability in real-world scenarios.
Developed models addressing the exploration-exploitation tradeoff, crucial for dynamic decision processes, investigated strategies to augment exploratory capabilities.
Translated theoretical frameworks into practical implementations, ensuring robust applicability.
Mastered skills in optimizing through neural networks, and strategically balancing exploration and exploitation.?
Helped upper management crystallize their understanding of the level of adoption and sustained use of the digital service application Mercedes me connect across global markets.
Taken over all the duties of a senior business analyst
Prepared and analyzed raw data sourced from the in-house data warehouse and QlikView.
Created and maintained visualization assets and reporting materials using MS Excel and MS PowerPoint.
Conducted the experiment on volunteers using the neurofeedback mobile application Auto Train Brain with an EEG measuring device collecting data.
Extracted raw data from an EEG measuring device.
Systematically prepared and analyzed the data.
Developed machine learning models for the neurofeedback mobile application Auto Train Brain using Python.
Training
Enhanced a deep reinforcement learning model?s performance and exploratory capabilities by introducing stochasticity onto deep neural networks using PyTorch on JetBrains PyCharm.
Conducted novel research in the domain of deep reinforcement learning for optimized decision-making in uncertain environments.
Explored the symbiosis between neural networks and stochastic elements such as Bayesian neural networks, enhancing adaptability in real-world scenarios.
Developed models addressing the exploration-exploitation tradeoff, crucial for dynamic decision processes, investigated strategies to augment exploratory capabilities.
Translated theoretical frameworks into practical implementations, ensuring robust applicability.
Mastered skills in optimizing through neural networks, and strategically balancing exploration and exploitation.?
Helped upper management crystallize their understanding of the level of adoption and sustained use of the digital service application Mercedes me connect across global markets.
Taken over all the duties of a senior business analyst
Prepared and analyzed raw data sourced from the in-house data warehouse and QlikView.
Created and maintained visualization assets and reporting materials using MS Excel and MS PowerPoint.
Conducted the experiment on volunteers using the neurofeedback mobile application Auto Train Brain with an EEG measuring device collecting data.
Extracted raw data from an EEG measuring device.
Systematically prepared and analyzed the data.
Developed machine learning models for the neurofeedback mobile application Auto Train Brain using Python.