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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models
| Date | Stars |
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| 2026-07-24 | 4316 |
| 2026-07-25 | 4316 |
| 2026-07-28 | 4316 |
| 2026-07-30 | 4316 |
| 2026-07-31 | 4323 |
| 2026-08-02 | 4324 |
| 2026-08-04 | 4326 |
| 2026-08-05 | 4328 |
| 2026-08-06 | 4328 |
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# Deep Learning Specialization on Coursera (offered by deeplearning.ai) Programming assignments and quizzes from all courses in the Coursera [Deep Learning specialization](https://www.coursera.org/specializations/deep-learning) offered by `deeplearning.ai`. Instructor: [Andrew Ng](http://www.andrewng.org/) ## Notes ### For detailed interview-ready notes on all courses in the Coursera Deep Learning specialization, refer [www.aman.ai](https://aman.ai/). ## Setup Run ```setup.sh``` to (i) download a pre-trained VGG-19 dataset and (ii) extract the zip'd pre-trained models and datasets that are needed for all the assignments. ## Credits This repo contains my work for this specialization. The code base, quiz questions and diagrams are taken from the [Deep Learning Specialization on Coursera](https://www.coursera.org/specializations/deep-learning), unless specified otherwise. ## 2021 Version This specialization was updated in April 2021 to include developments in deep learning and programming frameworks, with the biggest change being shifting from TensorFlow 1 to TensorFlow 2. This repo has been updated accordingly as well. ## Programming Assignments ### Course 1: Neural Networks and Deep Learning - [Week 2 - PA 1 - Python Basics with Numpy](https://nbviewer.jupyter.org/github/amanchadha/coursera-deep-learning-specialization/blob/master/C1%20-%20Neural%20Networks%20and%20Deep%20Learning/Week%202/Python%20Basics%20with%20Numpy/Python_Basics_With_Numpy_v3a.ipynb) - [Week 2 - PA 2 - Logistic Regression with a Neural Network mindset](https://nbviewer.jupyter.org/github/amanchadha/coursera-deep-learning-specialization/blob/master/C1%20-%20Neural%20Networks%20and%20Deep%20Learning/Week%202/Logistic%20Regression%20as%20a%20Neural%20Network/Logistic_Regression_with_a_Neural_Network_mindset_v6a.ipynb) - [Week 3 - PA 3 - Planar data classification with one hidden layer](https://nbviewer.jupyter.org/github/amanchadha/coursera-deep-learning-specialization/blob/master/C1%20-%20Neural%20Networks%20and%20Deep%20Learning/Week%203/Planar%20data%20classification%20with%20one%20hidden%20layer/Planar_data_classification_with_onehidden_layer_v6c.ipynb) - [Week 4 - PA 4 - Building your Deep Neural Network: Step by Step](https://nbviewer.jupyter.org/github/amanchadha/coursera-deep-learning-specialization/blob/master/C1%20-%20Neural%20Networks%20and%20Deep%20Learning/Week%204/Building%20your%20Deep%20Neural%20Network%20-%20Step%20by%20Step/Building_your_Deep_Neural_Network_Step_by_Step_v8a.ipynb) - [Week 4 - PA 5 - Deep Neural Network for Image Classification: Application](https://nbviewer.jupyter.org/github/amanchadha/coursera-deep-learning-specialization/blob/master/C1%20-%20Neural%20Networks%20and%20Deep%20Learning/Week%204/Deep%20Neural%20Network%20Application_%20Image%20Classification/Deep%20Neural%20Network%20-%20Application%20v8.ipynb) ### Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization - [Week 1 - PA 1 - Initialization](https://nbviewer.jupyter.org/github/amanchadha/coursera-deep-learning-specialization/blob/master/C2%20-%20Improving%20Deep%20Neural%20Networks%20Hyperparameter%20tuning%2C%20Regularization%20and%20Optimization/Week%201/Initialization/Initialization.ipynb) - [Week 1 - PA 2 - Regularization](https://nbviewer.jupyter.org/github/amanchadha/coursera-deep-learning-specialization/blob/master/C2%20-%20Improving%20Deep%20Neural%20Networks%20Hyperparameter%20tuning%2C%20Regularization%20and%20Optimization/Week%201/Regularization/Regularization_v2a.ipynb) - [Week 1 - PA 3 - Gradient Checking](https://nbviewer.jupyter.org/github/amanchadha/coursera-deep-learning-specialization/blob/master/C2%20-%20Improving%20Deep%20Neural%20Networks%20Hyperparameter%20tuning%2C%20Regularization%20and%20Optimization/Week%201/Gradient%20Checking/Gradient%20Checking%20v1.ipynb) - [Week 2 - PA 4 - Optimization Methods](https://nbviewer.jupyter.org/github/amanchadha/coursera-deep-learn
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f4ddf4e3bfad155e, topic:deep-learning, topic:neural-network