Kamil Atak

Equipped with strong problem solving abilities, willingness to learn, and excellent communication skills. Poised to contribute to team success and achieve positive results through analytical thinking and a proactive approach. Ready to tackle new challenges and advance organizational objectives with dedication, enthusiasm, and a passion for leveraging data driven insights to drive impactful decisions. Skilled in Python, SQL, data visualization, and machine learning, with experience in developing innovative solutions and collaborating effectively in dynamic environments.

• E-Commerce Website: Built a full-stack e-commerce platform using Node.js, React, and MongoDB. Implemented secure authentication with JWT, password hashing with Bcrypt.js, and responsive design with Bootstrap. Designed RESTful APIs and managed data with Mongoose.
• Custom Neural Network Library: Designed and implemented a modular C-based neural network library with support for various activation functions (e.g., sigmoid, ReLU, tanh) and backpropagation. Features include dynamic memory management, matrix operations, and batch processing for training.
• Automated Futures Trading System: Developed a Python-based system for backtesting and live trading futures contracts using Binance API, Pandas, NumPy, and Matplotlib. Implemented a moving average strategy with real-time data streaming via BinanceSocketManager and secure trade execution. Included data visualization with Matplotlib for analyzing backtesting results. Enhanced with historical data fetching, multi-level moving average analysis, and asynchronous programming for improved performance.
• Image Classification with Custom CNN: Developed a PyTorch-based model to classify handwritten digits from the MNIST dataset. Designed a modular Convolutional Neural Network (CNN) with ReLU activation, batch normalization, and layers like convolution, max-pooling, and fully connected layers. Utilized CrossEntropyLoss for loss calculation and the Adam optimizer with learning rate scheduling for training. Visualized training and validation performance using Matplotlib, achieving ~99% test accuracy after hyperparameter tuning.
• Mobile Application: Developed a mobile app to locate the cheapest products on a map using Google Maps and Places APIs. Implemented features like product search, price comparison, and user location integration. Utilized Kotlin for Android development, adhering to best practices for modern UI/UX design and performance optimization.

• Convolutional Neural Networks in TensorFlow
  Authorized by DeepLearning.AI and offered through Coursera
  Issued June 21, 2023
• Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
  Authorized by DeepLearning.AI and offered through Coursera
  Issued June 17, 2023