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angkonn / repository
This repository contains Jupyter notebooks from two Kaggle courses: 1. Intro to Deep Learning, 2. Computer Vision
This repository contains Jupyter notebooks from two Kaggle courses:
These notebooks cover essential concepts in deep learning and computer vision—from building neural networks to exploring convolutional techniques, pooling, and data augmentation for image processing.
Intro_to_Deep_Learning: Notebooks covering fundamental deep learning workflows, including network architecture design, activation functions, and model optimization.
Computer_Vision: Notebooks that dive into convolutional neural networks, sliding windows (stride and padding), custom convnets, and techniques to enhance image data.
You can use the notebooks as a reference for best practices in deep learning and computer vision projects.
Contributions are welcome! If you have improvements, additional content, or suggestions, please open an issue or submit a pull request and feel free to adjust any sections to better fit your project details or personal style.
Selected from shared topics, language and repository description—not editorial ratings.
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nady717 /
This repository contains a Jupyter Notebook implementing a Deep Learning-based Visual Question Answering (VQA) model. The project explores how neural networks can interpret images and answer related questions using NLP and Computer Vision techniques.