Benchmarking deep learning algorithms on many-core systems
Deep learning is a branch of machine learning that aims to extract multiple simple features from data and then combining the simple features and deriving increasingly more high level features to extract abstract data representations. In this study, we will be investigating a particular algorithm...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| التنسيق: | Final Year Project |
| اللغة: | English |
| منشور في: |
2016
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/10356/66556 |
| الوسوم: |
إضافة وسم
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| الملخص: | Deep learning is a branch of machine learning that aims to extract multiple
simple features from data and then combining the simple features and
deriving increasingly more high level features to extract abstract data
representations.
In this study, we will be investigating a particular algorithm of deep learning
known as a CNN (Convolutional Neural Network) for image and text
classification. Specifically, this study is to note how different values for the
parameters of the CNN will affect its training time and accuracy. We will also
be training CNNs on different devices like the Nvidia GeForce 840M and
Tesla K40 Graphic Processing Units (GPU) and seeing how they fare with
different data sets. Comparisons between tensor manipulation libraries like
Theano and TensorFlow will be made too, to see which is better and why.
We discovered an interesting method that may make training a CNN made in
Keras to be faster, and the results will be discussed in the following chapters. |
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