d2l-ai/berkeley-stat-157 resource

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Course homepage and materials for STAT 157, an Introduction to Deep Learning class taught at UC Berkeley in Spring 2019 by Alex Smola and Mu Li. It includes lecture slides, videos, Jupyter notebooks, homework assignments, and solutions built around the Dive into Deep Learning (D2L) book.

Use cases

• learn deep learning from scratch
• find a university-level deep learning course with homework
• get jupyter notebooks for practicing neural networks
• study deep learning theory with hands-on implementation
• self-study an introduction to deep learning course
• find lecture slides and videos on deep learning

When to choose

• you want a structured, semester-long introduction to deep learning with assignments
• you prefer learning through runnable Jupyter notebooks
• you want free lecture videos and slides from Berkeley instructors

When to avoid

• you need up-to-date material covering modern architectures like transformers or LLMs
• you want a maintained library or tool rather than course content
• you need a framework-agnostic or PyTorch-only curriculum (this course used MXNet)

Facets

learning-resource · maturity maintenance

deep-learning machine-learning data-science deep-learning machine-learning education tutorials python cross-platform jupyter-notebooks course-materials uc-berkeley mxnet d2l lectures homework

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