"Life is too short to dream small so dream big"
"Life is too short to dream small so dream big"
Practical Deep Learning Application: Utilized PyTorch to develop a tailored deep neural network for detecting planes in high-definition satellite images.
Informed Architecture Refinement: Conducted thorough research on architectures, strategically refining a selected baseline configuration.
Iterative Performance Enhancement: Employed an iterative development process, fine-tuning the framework based on research insights and hands-on experimentation.
Top 10% Accuracy Achievement: Attained top 10% class placement by optimizing the framework, showcasing proficiency in deep neural network training using PyTorch for computer vision tasks.
Practical Deep Learning Mastery: Gained hands-on experience in designing and training deep convolutional neural networks using PyTorch.
Baseline Enhancement: Elevated professor’s baseline architecture for precise image classification across 100 categories.
Iterative Architecture Refinement: Employed an iterative approach, adapting and honing the baseline for optimal image classification performance.
Top 10% Class Ranking: Achieved exceptional results, securing a top 10% class ranking for accuracy and proving expertise in PyTorch-based deep neural network design and training.
MNIST CNN for Character Recognition: Create a convolutional neural network (CNN) to perform numeric character recognition using the MNIST dataset.
Architecture and Function Overview: Implement a CNN for character recognition, with architectural insights provided. Additional details available at http://cs231n.stanford.edu.
Independent Problem Solving: Embrace the challenge by researching online resources and reverse-engineering template code for coding solutions in MATLAB.
Distinct Layer Types: Construct a CNN with four distinct layer types, enhancing the model’s capability for character recognition.
Igniting a passion for the future by imparting STEM knowledge to the next generation. Employing tailored teaching methods crafted to match each student's unique learning style for an engaging and personalized educational experience.
Teaching coding to kids through engaging lessons, helping them grasp programming languages and problem-solving concepts. While mentor students, guide them through coding projects, and maintain a positive learning environment. Monitor progress, communicate with parents, and stay updated on coding trends to provide effective instruction.
Engineered custom software solutions at Webb Electronics, collaborating across teams to analyze requirements, design, and implement efficient code. Played a key role in testing and refining software functionalities to enhance overall product performance and user satisfaction.
Concluded my studies at Simon Fraser University in June, emphasizing AI and databases. Enrolled in courses centered around computer vision, computer simulations, and databases, while also participating in classes dedicated to requirements engineering to enhance my skill set.