Insurance Agent
Lead insurance presentations and community building, collaborative reviewing of insurance policies, and presented policies for education and women's benefits.
Data Science Professional
Passionate Data Science student with expertise in analytics and predictive model building. Technical proficiency in Python, R, SQL, and machine learning libraries including sci-kit-learn, TensorFlow, PyTorch, and Keras. Experienced in data interpretation and problem-solving with a strong ability to adapt to new environments and work effectively in teams.
Lead insurance presentations and community building, collaborative reviewing of insurance policies, and presented policies for education and women's benefits.
Web Crawling, Graph Networks, Knowledge Management
Intelligent Knowledge Graph for Data Science Learning. Designed and developed a specialized knowledge graph platform using web crawling techniques, implementing a log-weighted graph network with token system for concept classification.
Python, TensorFlow, PyTorch, Pandas, NumPy, Matplotlib, Seaborn
No-Code Data Science Platform with drag-and-drop modules for data analysis, preprocessing, and model building. Integrated with TensorFlow, PyTorch, Pandas, and NumPy.
R, Shiny, Statistical Analysis
No-Code Statistical Analysis Tool built in R with interactive features for statistical analysis and data visualization.
Python, Linear Regression, Yahoo Finance, Sklearn
Stock price prediction model using Linear regression in machine learning.
Python, BeautifulSoup, yfinance, Streamlit
Website for stock price analysis and comparison between two stocks.
CGPA: 8.65, Focus areas: data preprocessing, statistical analysis, machine learning, deep learning
Convolutional Neural Networks (CNNs) is changing the various fields, particularly computer vision, demonstrating unparalleled performance in work like image detection and computer vision. However, achieving optimal CNN performance on selection of hyperparameters, which are critical yet not learned during training. Among these, the learning rate, optimizer, and activation function significantly influence a network's convergence speed, training stability, and ultimate generalization capability. While individual studies often focus on optimizing a single hyperparameter, the interplay and synergistic effects between these three crucial components are less comprehensively explored. This research empirically investigates how different combinations of learning rates, optimizers and activation functions collectively impact CNN training dynamics and performance. Through systematic experimentation on benchmark image datasets, we aim to identify optimal configurations and uncover the complex relationships that emerge when these hyperparameters are considered in concert. Our findings reveal that how a parameter change effect the performance of the model and also the dynamic learning rate affect the performance of model.
Technologies: PyTorch, TensorFlow, OpenCV, NumPy, Scikit-learn
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Call: (+91) 7888873321 Email: abhishek.k.jalandhar@gmail.com