This internship report gives an overview how about my experiences\u00a0at Siemens Corporation Corporate Technology with the development of an application using\u00a0a Neural Network for classification. It also compares the results a Support Vector Machine implementation summarizes the overall results.<\/p> <\/div>\n\n\n <\/div>\n \t\t<\/div>\n\t\t <\/div>\n \n <\/div>\n \n\t\t\n\t\t\n\t\t
Developing a Xamarin App for Handwritten Character Recognition using a Neural Network Company: Siemens Corporation Corporate Technology Institute: University of Applied Sciences Upper-Austria Field of Study: Software Engineering Author: Dinu Marius-Constantin Prelude This internship report gives an overview how about my experiences\u00a0at Siemens Corporation Corporate Technology with the development of an application using\u00a0a Neural […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_exactmetrics_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"spay_email":"","jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true},"categories":[108],"tags":[114,115,116],"jetpack_featured_media_url":"","jetpack_publicize_connections":[],"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p7SrVj-1b2","jetpack-related-posts":[{"id":5080,"url":"https:\/\/www.dinu.at\/profile\/home\/ensemble-learning-for-domain-adaptation-by-importance-weighted-least-squares\/","url_meta":{"origin":4528,"position":0},"title":"Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation","date":"13. August 2022","format":false,"excerpt":"Abstract We study the problem of choosing algorithm hyper-parameters in unsupervised domain adaptation, i.e., with labeled data in a source domain and unlabeled data in a target domain, drawn from a different input distribution. We follow the strategy to compute several models using different hyper-parameters, and, to subsequently compute a\u2026","rel":"","context":"In "General"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":4672,"url":"https:\/\/www.dinu.at\/profile\/home\/overcoming-catastrophic-forgetting-with-context-dependent-activations-xda-and-synaptic-stabilization\/","url_meta":{"origin":4528,"position":1},"title":"Overcoming Catastrophic Forgetting with Context-Dependent Activations (XdA) and Synaptic Stabilization","date":"25. November 2019","format":false,"excerpt":"Abstract Overcoming Catastrophic Forgetting in neural networks is crucial to solving continuous learning problems. Deep Reinforcement Learning uses neural networks to make predictions of actions according to the current state space of an environment. In a dynamic environment, robust and adaptive life-long learning algorithms mark the cornerstone of their success.\u2026","rel":"","context":"In "General"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":5142,"url":"https:\/\/www.dinu.at\/profile\/home\/a-neuro-symbolic-perspective-on-large-language-models-llms\/","url_meta":{"origin":4528,"position":2},"title":"A Neuro-Symbolic Perspective on Large Language Models (LLMs)","date":"22. January 2023","format":false,"excerpt":"We are excited to present our work, combining the power of a symbolic approach and Large Language Models (LLMs). Our Symbolic API bridges the gap between classical programming (Software 1.0) and differentiable programming (Software 2.0). Conceptually, our framework uses neural networks - specifically LLMs - at its core, and composes\u2026","rel":"","context":"In "General"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/www.dinu.at\/wp-content\/uploads\/2023\/01\/symai_logo.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":4567,"url":"https:\/\/www.dinu.at\/profile\/home\/deep-learning-script\/","url_meta":{"origin":4528,"position":3},"title":"Deep Learning Script","date":"19. April 2017","format":false,"excerpt":"NVIDIA DIGITS offers great support for\u00a0experimenting with Deep Learning and provides\u00a0great integration of Caffe Script. To improve this experience I developed a DSL for Caffe which eases the prototyping of network architectures by drastically reducing the amount of code line and simplifying the development. All the results are available on\u2026","rel":"","context":"In "General"","img":{"alt_text":"","src":"https:\/\/i2.wp.com\/www.dinu.at\/wp-content\/uploads\/2017\/04\/DLS-1024x651.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":4721,"url":"https:\/\/www.dinu.at\/profile\/home\/lighter-dependency-injection-with-pytorch\/","url_meta":{"origin":4528,"position":4},"title":"Lighter: Dependency Injection for PyTorch","date":"9. June 2021","format":false,"excerpt":"Python & Machine Learning: If you ever thought that handling all those kwargs, dictionaries of config properties, or huge method parameter listings feels tedious and somewhat like a step back in the evolution of programming language, well... then this is because you are completely right! It is a step back.\u2026","rel":"","context":"In "General"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":5072,"url":"https:\/\/www.dinu.at\/profile\/home\/reactive-exploration-to-cope-with-non-stationarity-in-lifelong-reinforcement-learning\/","url_meta":{"origin":4528,"position":5},"title":"Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning","date":"1. August 2022","format":false,"excerpt":"Abstract In lifelong learning, an agent learns throughout its entire life without resets, in a constantly changing environment, as we humans do. Consequently, lifelong learning comes with a plethora of research problems such as continual domain shifts, which result in non-stationary rewards and environment dynamics. These non-stationarities are difficult to\u2026","rel":"","context":"In "General"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]}],"builder_content":"
This internship report gives an overview how about my experiences\u00a0at Siemens Corporation Corporate Technology with the development of an application using\u00a0a Neural Network for classification. It also compares the results a Support Vector Machine implementation summarizes the overall results.<\/p> \n