Understanding Deep Learning
Deeply understand the principles and applications of deep learning
- Provide Python notebook exercises covering the entire book to help readers practice deep learning algorithms.
- Contains basic knowledge points such as supervised learning, shallow networks, deep networks, activation functions, etc.
- Introduced core concepts of deep learning such as loss function, optimization algorithm, and backpropagation.
- Provides in-depth discussions on advanced topics such as regularization techniques, convolutional networks, and self attention mechanisms.
- Explored unsupervised learning techniques such as generative adversarial networks, variational autoencoders, and diffusion models.
- Discussed the theoretical foundations of deep learning such as deep reinforcement learning, gradient flow, and neural tangent kernels.
Product Details
Understanding Deep Learning is a book that delves into the principles and applications of deep learning. It provides comprehensive content in the field of deep learning, including rich mathematical background knowledge, supervised learning, and the construction and training of neural networks. The Python notebook exercises provided in the book help readers deepen their understanding through practice. In addition, there are resources provided for teachers, including images, slides, and teaching aids.
