About Me
Passionate about creating functional, beautiful and user-friendly experiences.
Hello! I'm Debashish, an engineer with a passion for building innovative and user-friendly technologies. I've accomplished my Bachelor's degree in Computer Science and Engineering at Daffodil International University. AI and Machine Learning are my areas of interest. I'm currently working as a freelancer and building my own projects. I'm also a full-stack developer with a keen eye for design and a love for modern technologies. I specialize in creating dynamic, responsive web applications & mobile applications that provide exceptional user experiences.
When I'm not coding, you can find me exploring new technologies, contributing to open-source projects, or designing user interfaces that push the boundaries of what's possible on the web.
Skills & Technologies
Technologies I work with to bring ideas to life
Languages
Frontend
Backend
AI/ML
Tools & Platforms
Experience
Loading...
Currently Working On
Projects and initiatives I'm actively developing
An Incorporated Security System
Developing an AI-incorporated security system featuring real-time video surveillance, face recognition, anomaly detection, and secure access control. Built using Python, TensorFlow, OpenCV, React, Node.js, and cloud infrastructure on AWS, focusing on scalability, data security, and real-world deployment.
Open Source Contribution
Contributing to Datasets, Models, PyPl, GitHub, and open-source projects
GitHub Activity
My open-source contributions and coding activity
Code Snippets
Some of my favorite code snippets and utilities
React Custom Hook
A reusable debounce hook for React
function useDebounce<T>(value: T, delay: number): T {
const [debouncedValue, setDebouncedValue] = useState<T>(value);
useEffect(() => {
const handler = setTimeout(() => {
setDebouncedValue(value);
}, delay);
return () => {
clearTimeout(handler);
};
}, [value, delay]);
return debouncedValue;
}Python ML Model
Neural network model for binary classification
def train_model(X_train, y_train):
model = Sequential([
Dense(128, activation='relu', input_shape=(X_train.shape[1],)),
Dropout(0.3),
Dense(64, activation='relu'),
Dense(1, activation='sigmoid')
])
model.compile(
optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy']
)
model.fit(X_train, y_train, epochs=50, batch_size=32)
return modelCertifications & Achievements
Professional certifications and recognitions
Hobbies & Interests
What I enjoy when I'm not coding
Photography
Capturing moments and exploring creative perspectives
Reading
Tech books, sci-fi novels, and research papers
Gaming
Strategy games and indie titles
Hiking
Exploring nature and staying active
Music
Playing guitar and discovering new artists
Cooking
Experimenting with new recipes and cuisines
Stay Updated
Subscribe to my newsletter for the latest blog posts, projects, and tech insights.
Blog & Updates
My latest thoughts, insights, and updates from the world of technology.
Let's Connect
Have a project in mind? Let's discuss how we can work together.