Kulbear/deep-learning-coursera resource

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7735 stars · 5459 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A collection of completed programming assignments, quiz solutions, and notes for Andrew Ng's Deep Learning Specialization on Coursera, written as Jupyter Notebooks. It serves as a reference for learners working through the five-course specialization covering neural networks, hyperparameter tuning, and structured machine learning projects.

Use cases

• reference solutions for deep learning coursera assignments
• learn neural networks from scratch with python notebooks
• understand logistic regression as a neural network mindset
• study hyperparameter tuning and regularization examples
• prepare for the deep learning specialization quizzes
• find worked examples of building deep neural networks step by step

When to choose

• you are enrolled in or considering Andrew Ng's Deep Learning Specialization and want reference material
• you learn best from worked Jupyter Notebook examples with step-by-step implementations
• you want to check your own assignment solutions against a completed set

When to avoid

• you want a production deep learning library or framework
• you intend to copy assignment solutions rather than learn from them, which the author explicitly discourages
• you need up-to-date course content matching the current Coursera version, as the repo was last updated in 2019

Facets

learning-resource · maturity maintenance

deep-learning machine-learning deep-learning machine-learning tutorials python coursera andrew-ng jupyter-notebooks course-assignments educational

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