Technical Expertise

What I Work With

My technical knowledge in deep learning, computer vision, model training, and improving neural network performance.

Convolutional Neural Networks

Understanding and working with CNN architectures for image classification and computer vision tasks, including convolution, pooling, feature extraction, and classification layers.

CNN Convolution Pooling Computer Vision

Data Augmentation

Using transformations such as rotation, flipping, cropping, and scaling to create more diverse training data and improve model generalization.

Augmentation Generalization Image Data

Overfitting Prevention

Applying techniques that help neural networks generalize better instead of memorizing the training dataset.

Dropout Data Augmentation Regularization Early Stopping

Regularization

Regularization applied to models helps combat overfitting.

Dropout Weight Decay

Weight Decay

Weight decay can be used to regularize weights by penalizing large ones; this technique helps improve model accuracy and prevent overfitting.

Weight Decay

Dropout

Using dropout to prevent overfitting

Dropout

Hyperparameter Tuning

Working with parameters such as learning rate, batch size, number of epochs, optimizer settings, and network architecture to improve training.

Learning Rate Batch Size Epochs Optimizer

Addressing the vanishing gradient problem

Solving the vanishing gradient problem using techniques

Batch Normalization Batch Size Epochs Optimizer