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Understanding Deep Learning
Understanding Deep Learning
U
Understanding Deep Learning
AI coursesAI lessons#Deep Learning#Machine Learning#Education#PracticeStandard PicksOpen Source
Overview :
Understanding Deep Learning is a book that delves into the principles and applications of deep learning. It provides a wealth of mathematical background knowledge, supervised learning, the construction and training of neural networks, and comprehensive content in the field of deep learning. The Python notebooks provided in the book help readers deepen their understanding through practice. In addition, there are resources for teachers, including images, slides, and teaching materials.
Target Users :
Understanding Deep Learning is suitable for researchers, students, and practitioners in the field of deep learning. Whether you are a beginner or an experienced professional, you can gain a deep understanding of deep learning and practical guidance from it.
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Use Cases
Researchers use the mathematical models in the book to build new neural network architectures.
Students use notebook exercises to complete assignments for their deep learning courses.
Data scientists leverage the algorithms in the book to optimize their machine learning projects.
Features
Provides Python notebook exercises covering the entire book's content, helping readers practice deep learning algorithms.
Covers fundamental knowledge points such as supervised learning, shallow networks, deep networks, activation functions, etc.
Introduces core deep learning concepts such as loss functions, optimization algorithms, and backpropagation.
Provides in-depth discussions on advanced topics such as regularization techniques, convolutional networks, and self-attention mechanisms.
Explores unsupervised learning techniques such as generative adversarial networks, variational autoencoders, and diffusion models.
Discusses theoretical foundations of deep learning such as deep reinforcement learning, gradient flow, and neural tangent kernels.
How to Use
Visit the official website of Understanding Deep Learning.
Download the required Python notebook files and run them locally or in a Colab environment according to the instructions.
Read the theoretical knowledge in the book to understand the principles and algorithms of deep learning.
Complete the exercises in the notebooks, practice deep learning algorithms and observe the results.
Utilize the teaching resources provided by the book, such as slides and teaching materials, for teaching or self-study.
Participate in online community discussions to exchange learning experiences and insights with other readers.
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