deep-learning-coursera and Deep-Learning-Specialization-Coursera

The two repositories are competitors, as both offer solutions to the same assignments within the Deep Learning Specialization on Coursera by Andrew Ng.

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Adoption 10/25
Maturity 16/25
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Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 25/25
Stars: 7,713
Forks: 5,492
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
Stars: 462
Forks: 380
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: Apache-2.0
Archived Stale 6m No Package No Dependents
Stale 6m No Package No Dependents

About deep-learning-coursera

Kulbear/deep-learning-coursera

Deep Learning Specialization by Andrew Ng on Coursera.

Contains Jupyter notebooks implementing core deep learning concepts—from logistic regression and multi-layer perceptrons through CNNs (ResNets, Keras) and sequence models (RNNs)—alongside quiz materials across five course modules. Implementations use NumPy for foundational algorithms and TensorFlow/Keras for practical applications, covering optimization techniques (gradient descent, Adam), regularization, and batch normalization. Spans the full specialization curriculum from foundational neural network theory to advanced architectures for computer vision and natural language processing tasks.

About Deep-Learning-Specialization-Coursera

abdur75648/Deep-Learning-Specialization-Coursera

This repo contains the updated version of all the assignments/labs (done by me) of Deep Learning Specialization on Coursera by Andrew Ng. It includes building various deep learning models from scratch and implementing them for object detection, facial recognition, autonomous driving, neural machine translation, trigger word detection, etc.

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