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