Deep Learning Projects

Built With:

• Python – Core language for development
• TensorFlow & Keras – Deep learning model development
• NumPy – Numerical computations
• Matplotlib & Seaborn – Visualizing training metrics and encoded features
• Scikit-learn – Data preprocessing and model evaluation
• Jupyter Notebooks – Interactive experimentation

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Overview

This repository contains a collection of deep learning projects implemented from scratch and using Keras/TensorFlow. The focus is on learning and experimenting with different neural network architectures for both foundational understanding and practical application.

Each subdirectory includes a focused project with code, explanations, and model training/testing based on real or synthetic datasets.

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Core Topics Covered:

• Neural Networks → Dense, CNNs, RNNs, and Transformers
• Autoencoders → Dimensionality reduction, noise removal
• Transfer Learning → Fine-tuning pre-trained models
• Custom Layers & Functional API → Advanced Keras model design
• Data Augmentation → Image preprocessing for improved generalization
• Text Generation → Sequence modeling with Transformers
• Regression & Classification Models → Image and numerical data tasks

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Projects Included:

• Autoencoders/ → Denoising and latent space visualization
• CNN/ → Image classification and data augmentation with Keras
• Transformers/ → Building and training attention-based models
• Transfer-Learning/ → Pretrained VGG16 model for aircraft damage classification
• Custom-Layers/ → Implementing layers from scratch in Keras
• Functional-API/ → Model building with non-sequential architectures
• Transpose-Convolution/ → Working with upsampling and decoder blocks
• Regression/ → House/fuel price predictions with deep models

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Dependencies

pip install numpy matplotlib seaborn scikit-learn tensorflow keras

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License

This repository is licensed under the MIT License.