Welcome to my Deep Learning projects repository using TensorFlow. This collection includes my practice exercises, mini-projects, and experiments with various deep learning architectures, covering topics such as computer vision, natural language processing, time series prediction, and more.
DeepLearning-TensorFlow/
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├── 📁 notebooks/ # Jupyter Notebooks with annotated code
├── 📁 models/ # Saved model architectures and weights
├── 📁 datasets/ # Links or scripts to download datasets
├── 📁 utils/ # Helper functions and preprocessing scripts
├── 📁 experiments/ # Individual project implementations
├── requirements.txt # Python package dependencies
└── README.md # You're here!
🚀 Projects Included
Project Description Techniques
Image_Classifier_CNN A convolutional neural network trained on CIFAR-10 and MNIST datasets CNN, BatchNorm, Dropout
TextSentiment_RNN Sentiment analysis using RNN and LSTM layers on IMDB reviews Embedding, LSTM, TextVectorization
GAN_FashionMNIST Generative Adversarial Network to synthesize fashion images GANs, Discriminator, Generator
TimeSeries_Forecasting Forecasting stock price trends using LSTM networks Sliding window, LSTM
TransferLearning_ResNet Image classification using pretrained ResNet50 Transfer Learning, Fine-tuning
🛠️ Key Concepts Practiced
Neural Network Fundamentals (Forward/Backward Pass)
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs, LSTMs, GRUs)
Generative Adversarial Networks (GANs)
Transfer Learning with TensorFlow Hub/Keras
Custom Training Loops and Callbacks
TensorBoard Visualization
📚 Datasets Used
MNIST / Fashion-MNIST
CIFAR-10 / CIFAR-100
IMDB Movie Reviews
Custom datasets from Kaggle/UCI
⚙️ Setup Instructions
Clone the repository:
bash
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git clone https://github.com/yourusername/DeepLearning-TensorFlow.git
cd DeepLearning-TensorFlow
Create a virtual environment and install dependencies:
bash
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pip install -r requirements.txt
Run a sample notebook:
bash
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jupyter notebook notebooks/Image_Classifier_CNN.ipynb
📈 Future Work
Integrate TensorFlow with TFLite for mobile deployment
Explore attention mechanisms and Transformer models
Implement Explainable AI (XAI) for model interpretability
Optimize models for inference speed and memory
🤝 Contributions
Open to collaboration and feedback! Feel free to fork, star ⭐️, or raise issues.
📧 Contact
Noor Islam S. Mohammad
Graduate Student & Researcher
📫 Email: islam [dot] m [at] nyu [dot] edu
📜 License
This project is licensed under the MIT License.