Passionate Student of Machine Intelligence and Deep Learning: Fellowship.AI Application

Table of Contents

1. Introduction
2. Background and Education
3. Experience in Computer Vision
4. Internship at Pawn Incorporated
5. Interest in Deep Learning
6. Accomplishments in Academic Research
7. Passion for the Fellowship.ai Program
8. Introduction to the Project
9. Challenges in the Dataset
10. Model Architecture Explanation
11. Results and Analysis
12. Potential Improvements
13. Conclusion

Article

👉 Introduction

Hey everyone! My name is Kevin Capsey, and I am thrilled to submit my application for the Photoshop.ei program. As a student working towards my master's degree with a focus on machine intelligence and cognition, I have developed a deep passion for this field. In this article, I will provide an overview of my background, my experience in computer vision, and my interest in deep learning. I will also discuss my accomplishments, including my research projects and academic paper publication. Lastly, I will explain why the fellowship.ai program is of great interest to me and how I believe my dedication and hunger for knowledge make me a perfect fit for this program.

👉 Background and Education

Before pursuing my master's degree, I completed my undergraduate degree in computer science from the Visual Technological University in India. During my undergraduate studies, I had the incredible opportunity to work in a computer vision laboratory under one of my professors. This experience allowed me to gain valuable insights into image classification, segmentation, and object detection tasks. It sparked my fascination with the field and motivated me to explore further.

👉 Experience in Computer Vision

After my undergraduate studies, I landed an internship at Pawn Incorporated, a health tech company. During my time there, I worked on developing a face mask detection system. The system was subsequently deployed in decontamination booths across India during the COVID-19 pandemic. This experience not only honed my technical skills but also exposed me to the real-world impact of computer vision applications.

👉 Internship at Pawn Incorporated

Working on the face mask detection system fueled my interest in deep learning. I became particularly intrigued by single image super-resolution techniques. In fact, I conducted an in-depth study on technologies such as deep learn super-sampling and neural super-sampling. My research culminated in an academic paper, which was presented at the prestigious IEEE International Conference on Advanced Computing and Communication Systems and subsequently published in IEEE Explorer.

👉 Accomplishments in Academic Research

During my undergraduate capstone project, my team and I developed a pneumonia detection system using x-ray scans. Our project garnered recognition, and we were awarded our university's best project of the year. These experiences have not only enriched my knowledge but also fueled my passion for computer vision and deep learning.

👉 Passion for the Fellowship.ai Program

The fellowship.ai program aligns perfectly with my goals and aspirations. The projects undertaken by fellowship.ai, such as fetal gender masking, inspire me greatly. The opportunity to work on similar projects alongside passionate peers and experienced mentors excites me. I consider myself a dedicated and diligent individual with a hunger for continuous learning. Being selected as a fellow for fellowship.ai would be an incredible opportunity to further my skills and contribute to groundbreaking advancements in the field of AI.

👉 Introduction to the Project

Now let's move on to the project I have been working on. The project involves classifying images belonging to 102 different species. Each species exhibits variations in Shape, size, color, and background. The dataset poses several challenges, including class imbalance and variations in image sizes.

👉 Challenges in the Dataset

The dataset suffers from class imbalance, with some classes having a significantly larger number of images compared to others. This could potentially bias the model towards classes with more images and hinder its ability to generalize on classes with fewer images. Additionally, the images in the dataset have varying Dimensions, with some being larger than the desired output size.

👉 Model Architecture Explanation

To tackle this classification task, I employed a 150-model trained on the ImageNet database. The model consists of a flattened layer, three dense layers, and two dropout layers. The layers of the pre-trained model are frozen to prevent retraining, and only the layers I added are trained. Data augmentation techniques, such as random flipping and rotations, were applied to increase the diversity of the dataset. The images were resized to 224x224 pixels and rescaled to have pixel values between 0 and 1.

👉 Results and Analysis

The training and validation accuracies of the model show a promising convergence and a gradual decrease in loss. The final training accuracy is approximately 91%, while the validation accuracy is around 88%. Although impressive, there is still room for improvement. The state-of-the-art ResNet model achieves an accuracy of 97.5% on this dataset through hyperparameter tuning and additional data augmentation techniques.

👉 Potential Improvements

To further enhance the model's accuracy, a larger dataset and more complex models like ResNet101 and ResNet152 could be employed. Additional data augmentations, such as translation blurs and central crops, could also be applied to provide richer information to the model.

👉 Conclusion

In conclusion, my diverse educational background, hands-on experience in computer vision, and deep passion for the field make me an ideal candidate for the Photoshop.ei program. The project I have been working on showcases my technical skills and ability to tackle challenging tasks. I am eager to contribute to the fellowship.ai program and collaborate with fellow AI enthusiasts to push the boundaries of AI research and applications.

Highlights

• Background and education in computer science
• Experience in computer vision and deep learning
• Accomplishments in academic research and publication of an academic paper
• Passion for the fellowship.ai program and desire to work on impactful projects
• Introduction to the project and challenges in the dataset
• Explanation of the model architecture
• Results and potential improvements for the model's accuracy

FAQ

Q: What motivated you to pursue a master's degree in machine intelligence and cognition? A: As a computer science enthusiast, I was captivated by the potential of machine intelligence and cognition to transform various industries. It's fascinating how machines can learn and interact with the world, and I wanted to be at the forefront of this exciting field.

Q: How did your internship at Pawn Incorporated contribute to your professional growth? A: My internship at Pawn Incorporated allowed me to apply my computer vision skills to develop a face mask detection system during the COVID-19 pandemic. This experience not only enhanced my technical skills but also highlighted the real-world impact of computer vision applications in healthcare.

Q: What advancements do you believe can be made in the field of deep learning? A: Deep learning has already made significant strides, but there is still room for improvement. Advancements can be made in areas such as data augmentation techniques, model architecture optimization, and developing more efficient algorithms for training deep neural networks.

Q: How do you plan to overcome the class imbalance issue in the dataset? A: To tackle the class imbalance issue, techniques such as oversampling minority classes or using class weights during training can be implemented. Additionally, the collection of more data for underrepresented classes can help balance the dataset and improve model performance.

Q: What are your future aspirations in the field of AI? A: My future aspirations in the field of AI are to contribute to cutting-edge research and development. I see myself working on projects that have a tangible impact on society, whether it's in healthcare, robotics, or any other domain where AI can make a difference.