Publicação
Visualizing neural network architectures
| Resumo: | In the constantly evolving realms of Deep Learning and Machine Learning, the ascent of intricate and powerful neural network models has pushed the frontiers of computational intelligence to new heights. Nevertheless, the conspicuous absence of an accessible and user-friendly visualization tool has presented an imposing hurdle in the endeavor to comprehend and dissect these intricate and multifaceted architectures. In direct response to this critical challenge, this dissertation unveils a network visualizer platform: Neural Network Explorer. this platform has been developed in an attempt to offer a comprehensive solution to these complex problems. Through an exhaustive and in-depth analysis of existing tools and APIs, the Neural Network Explorer platform provides a streamlined and intuitive methodology for visualizing a diverse and extensive array of neural networks. This innovative tool empowers users to seamlessly unravel and decode the intricacies of the various layers and structures that compose these intricate model architectures, thereby fostering a deeper and more insightful understanding of the fundamental mechanisms that govern their functioning. By delving into the realm of cutting-edge network visualization techniques, the platform not only facilitates the seamless export of illustrative images but also grants access to a wealth of detailed and layer-specific information. This abundant and comprehensive data resource acts as a dynamic catalyst for researchers and developers, equipping them with the essential insights and understanding needed to adeptly navigate the landscape of deep learning models and harness the potential within. Through its robust exploration and innovative methodologies, this work provides researchers and practitioners with an indispensable and user-friendly tool for unraveling and exploring the complex and architectures that underscore the foundation of advanced computational intelligence. |
|---|---|
| Autores principais: | Tavares, Diogo de Oliveira Campos |
| Assunto: | Machine learning Deep Learning Visualization |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | In the constantly evolving realms of Deep Learning and Machine Learning, the ascent of intricate and powerful neural network models has pushed the frontiers of computational intelligence to new heights. Nevertheless, the conspicuous absence of an accessible and user-friendly visualization tool has presented an imposing hurdle in the endeavor to comprehend and dissect these intricate and multifaceted architectures. In direct response to this critical challenge, this dissertation unveils a network visualizer platform: Neural Network Explorer. this platform has been developed in an attempt to offer a comprehensive solution to these complex problems. Through an exhaustive and in-depth analysis of existing tools and APIs, the Neural Network Explorer platform provides a streamlined and intuitive methodology for visualizing a diverse and extensive array of neural networks. This innovative tool empowers users to seamlessly unravel and decode the intricacies of the various layers and structures that compose these intricate model architectures, thereby fostering a deeper and more insightful understanding of the fundamental mechanisms that govern their functioning. By delving into the realm of cutting-edge network visualization techniques, the platform not only facilitates the seamless export of illustrative images but also grants access to a wealth of detailed and layer-specific information. This abundant and comprehensive data resource acts as a dynamic catalyst for researchers and developers, equipping them with the essential insights and understanding needed to adeptly navigate the landscape of deep learning models and harness the potential within. Through its robust exploration and innovative methodologies, this work provides researchers and practitioners with an indispensable and user-friendly tool for unraveling and exploring the complex and architectures that underscore the foundation of advanced computational intelligence. |
|---|