coursera-deep-learning-specialization

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Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai

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Open Source

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About coursera-deep-learning-specialization

This repository contains notes, programming assignments, and quizzes from all five courses of the Coursera Deep Learning Specialization offered by deeplearning.ai and taught by Andrew Ng. It covers Course 1: Neural Networks and Deep Learning, Course 2: Improving Deep Neural Networks (Hyperparameter tuning, Regularization and Optimization), Course 3: Structuring Machine Learning Projects, Course 4: Convolutional Neural Networks, and Course 5: Sequence Models. The assignments are implemented in Python using TensorFlow 2 (updated in April 2021). The repository includes a setup script to download pre-trained models and datasets (e.g., VGG-19) and references detailed interview-ready notes available at www.aman.ai. Note: Course 3 has no programming assignments but includes case study quizzes.

Key Features

Covers all 5 courses of the Deep Learning Specialization (C1–C5)

Includes programming assignments for each course (except C3) with Python/TensorFlow 2 code

Contains quizzes and case study questions for Course 3

Provides a setup script (setup.sh) to download pre-trained VGG-19 and other models

References detailed interview-ready notes on www.aman.ai

Updated to TensorFlow 2 (2021 version)

Pros & Cons

Pros

Comprehensive collection of all assignments from a renowned specialization
Well-organized by course and week for easy navigation
Includes both NumPy-based and TensorFlow implementations
Free and open source under a public GitHub repository
Useful for interview preparation with supplementary notes link

Cons

Forked repository, not original work by the uploader
No programming assignments for Course 3 (Structuring Machine Learning Projects)
Requires manual execution of setup.sh to download pre-trained models
Some assignments may rely on older dataset formats (e.g., VGG-19)

Best For

Learning deep learning fundamentals from course assignmentsReference implementation for neural networks, CNNs, RNNs, and sequence modelsPracticing building deep learning models from scratch using NumPy and TensorFlowStudying hyperparameter tuning, regularization, and optimization techniques

FAQ

What courses are covered in this repository?

All five courses of the Coursera Deep Learning Specialization: Neural Networks and Deep Learning, Improving Deep Neural Networks, Structuring Machine Learning Projects, Convolutional Neural Networks, and Sequence Models.

Are there programming assignments for Course 3?

No, Course 3 (Structuring Machine Learning Projects) has no programming assignments, but the repository includes case study quizzes from that course.

What version of TensorFlow is used?

The repository has been updated to TensorFlow 2 (as of April 2021). Earlier versions used TensorFlow 1.

Where can I find additional detailed notes?

Interview-ready detailed notes are available at www.aman.ai, as referenced in the repository.

How do I set up the pre-trained models?

Run the setup.sh script included in the repository. It downloads a pre-trained VGG-19 dataset and extracts zip'd pre-trained models and datasets needed for the assignments.