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You searched for subject:(TFLearn). Showing records 1 – 3 of 3 total matches.

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California State University – Sacramento

1. Shiroor, Shekhar Vikas. Implementation of a neural network agent that plays video games using reinforcement learning.

Degree: MS, Computer Science, 2019, California State University – Sacramento

This project to implements a generalized neural network agent that plays different video games using reinforcement learning algorithm. This project uses OpenAIs simulated video game environment ???gym??? for training and testing the proposed reinforcement learning algorithm solution. For training the neural network agent, different neural network models are used like Convolutional Neural Networks, Recurrent Neural Networks and combination of both. Python and TFlearn (Tensorflow backend) are used to implement the project. The results show that the proposed solution works well for an average of two to three games. However, performance of the solution is degraded when neural network is trained on four or more video games. Although using the ???TopK??? metric (which is added to the proposed solution to increase the efficiency of neural network to play multiple video games) yields a dramatic increase in training and validation accuracy of the neural networks, the networks are still not able to play variety of video games with good degree of precision. To improve the performance, deeper neural network models like VGG-19 can be utilized in the future, given that hardware resources required by such models are available (e.g., a GPU with a larger global memory is needed). Advisors/Committee Members: Muyan-Ozcelik, Pinar.

Subjects/Keywords: Deep learning; OpenAI; Reinforcement learning; Tensorflow; TFLearn; NumPy; LSTM

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Shiroor, S. V. (2019). Implementation of a neural network agent that plays video games using reinforcement learning. (Masters Thesis). California State University – Sacramento. Retrieved from http://hdl.handle.net/10211.3/207667

Chicago Manual of Style (16th Edition):

Shiroor, Shekhar Vikas. “Implementation of a neural network agent that plays video games using reinforcement learning.” 2019. Masters Thesis, California State University – Sacramento. Accessed February 28, 2021. http://hdl.handle.net/10211.3/207667.

MLA Handbook (7th Edition):

Shiroor, Shekhar Vikas. “Implementation of a neural network agent that plays video games using reinforcement learning.” 2019. Web. 28 Feb 2021.

Vancouver:

Shiroor SV. Implementation of a neural network agent that plays video games using reinforcement learning. [Internet] [Masters thesis]. California State University – Sacramento; 2019. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10211.3/207667.

Council of Science Editors:

Shiroor SV. Implementation of a neural network agent that plays video games using reinforcement learning. [Masters Thesis]. California State University – Sacramento; 2019. Available from: http://hdl.handle.net/10211.3/207667

2. Vidmark, Stefan. Röstigenkänning med Movidius Neural Compute Stick.

Degree: Applied Physics and Electronics, 2018, Umeå University

Företaget Omicron Ceti AB köpte en Intel Movidius Neural Compute Stick (NCS), som är en usb-enhet där neurala nätverk kan laddas in för att processa data. Min uppgift blev att studera hur NCS används och göra en guide med exempel. Med TensorFlow och hjälpbiblioteket TFLearn gjordes först ett testnätverk för att prova hela kedjan från träning till användning med NCS. Sedan tränades ett nätverk att kunna klassificera 14 olika ord. En mängd olika utformningar på nätverket testades, men till slut hittades ett exempel som blev en bra utgångspunkt och som efter lite justering gav en träffsäkerhet på 86% med testdatat. Vid inläsning i mikrofon så blev resultatet lite sämre, med 67% träffsäkerhet. Att processa data med NCS tog längre tid än med TFLearn men använde betydligt mindre CPU-kraft. I mindre system såsom en Raspberry Pi går det däremot inte ens att använda TensorFlow/TFLearn, så huruvida det är värt att använda NCS eller inte beror på det specifika användningsscenariot.

Omicron Ceti AB company had an Intel Movidius Neural Compute Stick (NCS), which is a usb device that may be loaded with neural networks to process data. My assignment was to study how NCS is used and to make a guide with examples. Using TensorFlow and the TFLearn help library a test network was made for the purpose of trying the work pipeline, from network training to using the NCS. After that a network was trained to classify 14 different words. Many different configurations of the network were tried, until a good example was found that was expanded upon until an accuracy of 86% with the test data was reached. The accuracy when speaking into a microphone was a bit worse at 67%. To process data with the NCS took a longer time than with TFLearn but used a lot less CPU power. However it’s not even possible to use TensorFlow/TFLearn in smaller systems like a Raspberry Pi, so whether it’s worth using the NCS depends on the specific usage scenario.

Subjects/Keywords: movidius; röstigenkänning; NCS; neurala nätverk; maskininlärning; TFLearn; Computer Systems; Datorsystem; Embedded Systems; Inbäddad systemteknik

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Vidmark, S. (2018). Röstigenkänning med Movidius Neural Compute Stick. (Thesis). Umeå University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-151032

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Vidmark, Stefan. “Röstigenkänning med Movidius Neural Compute Stick.” 2018. Thesis, Umeå University. Accessed February 28, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-151032.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Vidmark, Stefan. “Röstigenkänning med Movidius Neural Compute Stick.” 2018. Web. 28 Feb 2021.

Vancouver:

Vidmark S. Röstigenkänning med Movidius Neural Compute Stick. [Internet] [Thesis]. Umeå University; 2018. [cited 2021 Feb 28]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-151032.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Vidmark S. Röstigenkänning med Movidius Neural Compute Stick. [Thesis]. Umeå University; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-151032

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Linnaeus University

3. Strutynskiy, Maksym. A concept of an intent-based contextual chat-bot with capabilities for continual learning.

Degree: computer science and media technology (CM), 2020, Linnaeus University

Chat-bots are computer programs designed to conduct textual or audible conversations with a single user. The job of a chat-bot is to be able to find the best response for any request the user issues. The best response is considered to answer the question and contain relevant information while following grammatical and lexical rules. Modern chat-bots often have trouble accomplishing all these tasks. State-of-the-art approaches, such as deep learning, and large datasets help chat-bots tackle this problem better. While there is a number of different approaches that can be applied for different kind of bots, datasets of suitable size are not always available. In this work, we introduce and evaluate a method of expanding the size of datasets. This will allow chat-bots, in combination with a good learning algorithm, to achieve higher precision while handling their tasks. The expansion method uses the continual learning approach that allows the bot to expand its own dataset while holding conversations with its users. In this work we test continual learning with IBM Watson Assistant chat-bot as well as a custom case study chat-bot implementation. We conduct the testing using a smaller and a larger datasets to find out if continual learning stays effective as the dataset size increases. The results show that the more conversations the chat-bot holds, the better it gets at guessing the intent of the user. They also show that continual learning works well for larger and smaller datasets, but the effect depends on the specifics of the chat-bot implementation. While continual learning makes good results better, it also turns bad results into worse ones, thus the chat-bot should be manually calibrated should the precision of the original results, measured before the expansion, decrease.

Subjects/Keywords: Machine learning; intent based; chat-bot; dialogue systems; rule based; Python; TensorFlow; TFLearn; continual learning; online learning; supervised learning; unsupervised learning; IBM Watson; Watson Assistant; Computer Sciences; Datavetenskap (datalogi)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Strutynskiy, M. (2020). A concept of an intent-based contextual chat-bot with capabilities for continual learning. (Thesis). Linnaeus University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-99102

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Strutynskiy, Maksym. “A concept of an intent-based contextual chat-bot with capabilities for continual learning.” 2020. Thesis, Linnaeus University. Accessed February 28, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-99102.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Strutynskiy, Maksym. “A concept of an intent-based contextual chat-bot with capabilities for continual learning.” 2020. Web. 28 Feb 2021.

Vancouver:

Strutynskiy M. A concept of an intent-based contextual chat-bot with capabilities for continual learning. [Internet] [Thesis]. Linnaeus University; 2020. [cited 2021 Feb 28]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-99102.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Strutynskiy M. A concept of an intent-based contextual chat-bot with capabilities for continual learning. [Thesis]. Linnaeus University; 2020. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-99102

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.