Welcome to my advanced deep learning projects repository. This collection showcases a range of projects that demonstrate the application of deep learning techniques to solve various real-world problems. These projects include CNN and RNN architectures for image classification and digit recognition, as well as models for customer churn prediction, fake news detection, and more. Each project highlights my skills in data preprocessing, model building, training, and evaluation.
Objective: Implement and compare three different Convolutional Neural Network (CNN) architectures using the TensorFlow library, trained and evaluated on the CIFAR-10 dataset.
Tools/Libraries Used: TensorFlow, Keras, Matplotlib, Pandas, NumPy.
Key Insights:
- Model Comparison: The Deeper CNN with Dropout achieved the highest accuracy, indicating the benefits of deeper architectures with regularization.
- Effectiveness of Data Augmentation: Demonstrated competitive performance, enhancing model generalization.
- Training and Validation Trends: Provided insights into the models' learning behavior and areas for further optimization.
Objective: Implement and compare three different Recurrent Neural Network (RNN) architectures using the TensorFlow library, trained and evaluated on the MNIST dataset.
Tools/Libraries Used: TensorFlow.
Key Insights:
- Model Comparison: The GRU model achieved the highest accuracy, showcasing its effectiveness in capturing temporal dependencies.
- Training Efficiency: The LSTM and GRU models outperformed the Simple RNN, highlighting the advantages of advanced RNN architectures.
- Data Preprocessing: Normalizing pixel values and reshaping input data were crucial for effective training.
Objective: Predict customer churn using a deep learning model built with the TensorFlow library, evaluating model performance using various metrics.
Tools/Libraries Used: Pandas, Scikit-learn, TensorFlow, Keras.
Key Insights:
- Model Performance: Achieved 86.1% accuracy, with precision of 69.1%, recall of 52.9%, and F1 score of 59.9%.
- Feature Importance: Potential for improvement by exploring feature importance and engineering new features.
- Data Preprocessing: Emphasized the importance of scaling and encoding for effective model training.
Objective: Create a machine learning model to recognize handwritten digits using the MNIST dataset.
Tools/Libraries Used: NumPy, Matplotlib, TensorFlow, Keras, Scikit-learn, Seaborn.
Key Insights:
- Model Performance: Achieved high accuracy (97.42%) in recognizing handwritten digits, indicating its effectiveness.
- Data Preprocessing: Normalizing pixel values and reshaping images were crucial for model training.
- Regularization: Adding a dropout layer helped prevent overfitting and improved model generalization.
Objective: Implement a Convolutional Neural Network (CNN) to classify images from the CIFAR-10 dataset.
Tools/Libraries Used: TensorFlow, Keras, Matplotlib, Pandas, NumPy.
Key Insights:
- Model Performance: Achieved notable accuracy in classifying CIFAR-10 images, demonstrating the effectiveness of CNNs for image classification tasks.
- Data Augmentation: Enhanced model generalization through techniques like random flips and rotations.
- Layer Customization: Explored different layer configurations to optimize the model’s performance.
Objective: Predict whether news stories are likely to be fake using a deep learning model.
Tools/Libraries Used: Pandas, NumPy, Matplotlib, Seaborn, TensorFlow, Keras, NLTK, SpaCy.
Key Insights:
- Model Performance: Achieved high validation accuracy, indicating its effectiveness in predicting fake news.
- Data Preprocessing: Proper cleaning and preprocessing of text data were crucial for model training.
- Early Stopping: Implementing early stopping helped prevent overfitting and improved model generalization.
Objective: Create a deep learning model to classify images of cats and dogs.
Tools/Libraries Used: TensorFlow, Keras, Matplotlib, Google Colab, Kaggle API.
Key Insights:
- Model Performance: Achieved high accuracy on the training set and reasonable accuracy on the validation set.
- Data Preprocessing: Normalizing the image data was crucial for model performance.
- Early Stopping: Implementing early stopping could further improve model generalization and prevent overfitting.
Objective: Utilize natural language processing techniques to find similar words based on their relationships in a text corpus.
Tools/Libraries Used: NumPy, Pandas, Seaborn, Matplotlib, re, NLTK, Gensim, Flask, pyngrok.
Key Insights:
- Model Performance: The Word2Vec model accurately captures semantic relationships between words.
- Data Preprocessing: Thorough cleaning and preprocessing of text data is crucial for model performance.
- Deployment: Using Flask and pyngrok facilitates easy web deployment and access.
Objective: Create a content-based recommendation system to provide personalized movie recommendations to users.
Tools/Libraries Used: NumPy, Pandas, ast, Pickle, Scikit-learn, Flask, pyngrok.
Key Insights:
- Model Performance: The content-based recommendation model accurately captures relationships between movies based on their features.
- Data Preprocessing: Thorough cleaning and preprocessing of movie data is crucial for model performance.
- Deployment: Using Flask and pyngrok facilitates easy web deployment and access.
This repository highlights my ability to apply advanced deep learning techniques to solve diverse problems. Each project demonstrates a strong foundation in data preprocessing, model building, training, and evaluation. I am confident that these projects reflect my readiness for a challenging role in deep learning and data science.
Feel free to explore each project and reach out if you have any questions or feedback!