Deep Learning for Fault Diagnosis Part 3: Neural Network Classification | t-SNE Visualization
#machinelearning #faultdetection #dataanalysis #exploratorydataanalysis
#conditionmonitoring #predictivemaintenance
We will train the deep neural network using the training set and evaluate its performance on the testing set. We will use the accuracy metric to measure the performance of the model. Finally, we will use t-SNE, a popular dimensionality reduction technique, to visualize the high-dimensional output of the neural network in a two-dimensional space. This will help us gain insights into the structure of the data and the performance of the model.
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GitHub (Jupyter Notebook file of this video) - https://github.com/mohan696matlab/Pro...
dataset link - https://www.kaggle.com/datasets/averk...
Full Playlist - • Machine Learning for Fault Diagnosis:...
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Hello everyone! My name is Mohan, and I'm currently pursuing my PhD in artificial intelligence. My research focuses on fault diagnosis of green hydrogen multi-source hybrid systems, which is an exciting field that contributes to the development of sustainable energy technologies.
E-mail - [email protected]
Google Scholar - https://scholar.google.com/citations?...
LinkedIn - / balyogi-mohan-dash
GitHub - https://github.com/mohan696matlab?tab...
