Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
Covers hand pose estimation, regression techniques, and the evolution of image classification models from LeNet to VGG19.
Explores the mathematics of deep learning, neural networks, and their applications in computer vision tasks, addressing challenges and the need for robustness.
Introduces Convolutional Neural Networks, covering fully connected layers, convolutions, pooling, PyTorch translations, and applications like hand pose estimation and tubularity estimation.
