Tools Intermediate 1 year experience
Summary
Experience with TensorFlow and Keras for building and training convolutional neural networks. Applied to image classification achieving 84% accuracy on CIFAR-10 benchmark.
How I Apply This Skill
- Built CNN architectures with progressive convolutional layers (32->64->128 filters)
- Implemented regularization strategies with Dropout and MaxPooling to minimize overfitting
- Used Keras Sequential API for model construction and training
- Configured training with Adam optimizer, categorical cross-entropy loss, and train/test splitting
Key Strengths
- Model Building: Keras Sequential API, layer stacking, architecture design
- CNN Components: Conv2D, MaxPooling2D, Dropout, Dense, Flatten layers
- Training Configuration: Optimizers, loss functions, batch size, epochs
- Regularization: Dropout rates, validation splits, preventing overfitting
- Evaluation: Accuracy and loss metrics, confusion matrices