- Deployed Model to monitor and detect irregular
log events in CT and MRI machines.
- Integrated Transformer-based models and text
data augmentation to boost model performance
and event detection capabilities.
- Used PyT…
- Deployed Model to monitor and detect irregular
log events in CT and MRI machines.
- Integrated Transformer-based models and text
data augmentation to boost model performance
and event detection capabilities.
- Used PyTorch for the development and training
of deep learning model, ensuring high
performance.
Tool and Technologies used - Python, NLP
techniques, Transformer model, Data
augmentation for text, PyTorch.
<p>- Engineered a comprehensive diagnostic tool to</p><p>analyze error messages from CT scan machinery</p><p>and output the potential cause and recommended</p><p>actions for that error.</p><p>- Integrated Open source LLM models like Mistral</p><p>and Llama and employed prompt engineering for</p><p>effective language model utilization.</p><p>- Adopted a Retrieval-Augmented Generation</p><p>(RAG) using langchain framework to augment</p><p>large language model outputs with relevant data</p><p>for insightful error analysis and guidance.</p><p>Tool and Technologies used - Python, NLP</p><p>techniques, LLM model, prompt engineering,</p><p>RAG( Retrieval-Augmented Generation ),</p><p>Langchain.</p>
Engineered a comprehensive diagnostic tool to analyze error messages from CT scan machinery and output the potential cause and recommended actions for that error., Integrated Open source LLM models like Mistral and Llama and employed prompt engineering for effective language model utilization., Adopted a Retrieval-Augmented Generation (RAG) using langchain framework to augment large language model outputs with relevant data for insightful error analysis and guidance.
Deployed Model to monitor and detect irregular log events in CT and MRI machines., Integrated Transformer-based models and text data augmentation to boost model performance and event detection capabilities., Used PyTorch for the development and training of deep learning model, ensuring high performance.
<p>Image Captioning: (08/2022 - 12/2022)</p><p>Image caption generator model using convolutional neural</p><p>network(CNN) and Recurrent neural network(RNN), where it</p><p>uses both the concepts of Computer Vision and Neural</p><p>Language Processing(NLP).</p><p>Flicker8K was used as the dataset and the project used</p><p>libraries like tensorflow and keras for implementing CNN</p><p>and RNN neural network.</p><p></p>
<p>Novel Coronavirus Prediction (02/2022 - 04/2022)</p><p>Machine learning algorithm naive bayes classifier was used</p><p>to predict if person is covid positive or not based on their</p><p>symptoms.</p><p>Dataset was available in kaggle and libraries used were</p><p>sklearn, pandas, seaborn and matplotlib.</p>
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