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Application of a convolutional neural network (CNN) to accurately classify urban sounds in a bid to increase pedestrian safety using the UrbanSound8k dataset.

License: MIT License

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convolutional-neural-networks deep-learning mfcc-features pedestrian-safety urbansound8k jupyter-notebook python

urban-sound-classification-using-convolutional-neural-networks's Introduction

Hi there ๐Ÿ‘‹

A recent graduate from Concordia University Montreal, holding a Master's degree in Electrical & Computer Engineering, I am fueled by a profound passion for Machine Learning & Data Science. Actively immersed in captivating projects, I focus on deploying efficient end-to-end machine learning workflows across diverse domains, encompassing areas such as computer vision, predictive modeling, classification, regression, and more. Beyond my expertise in machine learning, I possess a strong foundation in data analytics and processing, gained through hands-on experience. I eagerly embrace opportunities to utilize tools like relational databases and business intelligence tools, leveraging them to advance project objectives.

Skills

  • Programming Languages: C++, HTML, Java, Python, SQL

  • Tools and Frameworks: Microsoft Azure, AWS, Docker, Git, GitHub, Keras, Jira, Microsoft Office 365, SQL Server, PostgreSQL, CosmosDB, MATLAB, Matplotlib, Numpy, ONNX, OpenVINO, Pandas, MS Power BI, PyTorch, Seaborn, Scikit-Learn, TensorFlow, TensorRT

  • Technical Skills: Academic Research, Computer Networks, Communication Skills, Data Modeling, Database Management, Data Science, Data Visualization, Deep Learning Frameworks, Machine Learning Algorithms, Natural Language Processing, Performance Tuning, Predictive Modeling, Statistical Analysis, Software Engineering

How to reach me: LinkedIn Profile

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