This Hand gesture recognition project using mediapipe is developed to recognize various hand gestures. The user can custom train any number of various hand gestures to train a model.

This application uses deep learning techniques to accurately classify brain tumor images. It has been trained on a diverse dataset, enabling it to predict the presence and type of tumors with high accuracy.

Collection of TensorFlow/Keras Jupyter notebooks demonstrating low-level APIs, custom training loops, callbacks, subclassed models, custom loss functions, transfer learning, and advanced deep learning architectures.

A collection of TensorFlow/Keras notebooks demonstrating custom model subclassing and custom layer creation. Includes regression on California Housing and filtered MNIST classification, showcasing preprocessing, training, evaluation, and deep learning customization skills.

A comparative study of the benefits of transfer learning over building a custom CNN architecture for a very small dataset.

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A collection of projects demonstrating deep TensorFlow/Keras expertise. Includes custom Dense & Conv2D layers and tf.GradientTape training loops for image classification (MNIST, CIFAR-10, Eurosat). Emphasizes model optimization, regularization, and core framework mechanics.

Hint of Realism, Welcome to Immersion

A comparative study of the benefits of transfer learning over building a custom CNN architecture for a very small dataset.