project.md

Project

Academic year: 2025-2026

Project Description: Build a Deep Learning Pipeline with TensorFlow

Objective
Develop a complete deep learning pipeline using TensorFlow. Choose one domain—text, audio, or images—select a suitable dataset, and define a specific subject for your project. Develop a complete pipeline, including data preprocessing, model design, training, evaluation, and deployment. Enhance your project by integrating Symbolic AI components for added functionality or interpretability.

---

Steps to Complete the Project

1. Choose Your Domain and Subject

   • Select a domain: text, audio, or images.
   • Define a clear and specific subject related to your chosen domain. Examples include:
     • Text: emotion analysis, paraphrasing, question answering.
     • Images: image classification, object detection.
     • Audio: speech recognition, emotion detection, music vs. speech classification.

2. Select a Dataset

   • Pick a dataset from the provided sources or propose your own.
   • Ensure the dataset is relevant to your chosen domain and task.

3. Preprocess the Data

   • Clean, transform, and augment the data as needed.
   • Use TensorFlow tools like tf.data or tf.keras.preprocessing for efficient pipelines.
   • For text, consider tokenization or embedding; for images, apply normalization or augmentation; for audio, extract features like spectrograms or MFCCs.

4. Design and Train Your Model

   • Build a model suitable for your task:
     • For text: use RNNs, LSTMs, or Transformers.
     • For images: use CNNs or pre-trained architectures like ResNet.
     • For audio: combine feature extraction layers with RNNs or CNNs.
   • Experiment with hyperparameters, activation functions, and layers.
   • Train your model using TensorFlow and evaluate its performance on a validation set.

5. Integrate Symbolic AI (Optional Bonus)

   • Combine your model with rule-based or logic-driven systems to improve interpretability or accuracy.
   • For example:
     • Use knowledge graphs in text analysis.
     • Add reasoning components for emotion recognition in audio.
     • Implement rule-based constraints for object detection in images.

6. Evaluate and Deploy

   • Assess your model using metrics appropriate to your task (e.g., accuracy, precision, recall).
   • Deploy your model as an interactive application or notebook.

---

Deliverables

1. A complete TensorFlow implementation of your pipeline.
2. A detailed report covering:
   • The chosen subject, problem statement, and objectives.
   • The dataset used.
   • Preprocessing methods.
   • Model architecture and training process.
   • Evaluation results and potential improvements.
3. A deployed demo or app.

Example Notebooks

• Data Processing in Tensorflow
• Handwriting recognition using MNIST dataset
• Text classification based on IMDB reviews
• Understanding Property Translation of Wikidata

Project domains

• Text
• Images
• Audio

Datasets

Existing catalogues

• https://www.kaggle.com/datasets
• https://www.tensorflow.org/datasets
• https://wordnet.princeton.edu/download
• http://www.image-net.org/

Domains

• Text:

  • Start with datasets like IMDB reviews, SQuAD, or CoNLL-2003.
  • Use pre-trained embeddings like GloVe, Word2Vec, or BERT.

• Images:

  • Use datasets such as CIFAR-10, ImageNet, or Oxford Flowers.
  • Try transfer learning with TensorFlow’s pre-trained models.

• Audio:

  • Choose datasets like LibriSpeech or UrbanSound8K.
  • Preprocess with audio-specific techniques like spectrograms.

Possible topics

• Text
  • Language identification
  • Speaker identification
  • Question answering
    • yes or no answering
    • answers to questions related to multiline paragraphs
    • mathematical question answering
  • Analysis of citations
  • Analysis of reviews
  • Paraphrasing
  • Common knowledge facts
  • Common sense explanation
  • Analysis of emotions
• Images
  • Object detection
  • Image classification
• Audio
  • Detection of music genre
  • Analysis of musical notes
    • pitch, timbre, envelope, etc.
  • Analysis of sentiments
  • Speech recognition
    • Single speaker
    • Multiple speakers
    • Accents
  • Emotion recognition
  • Distinction between speech and music
  • Speech commands
  • Transcription