Deep Learning Specialization by Andrew Ng on Coursera
Master Deep Learning , Break Into AI!
Instructor: Andrew Ng
This repository contains all my work for this specialization. All the code base and quiz questions are taken from Deep Learning Specialization On Coursera
Course 1: Neural Networks and Deep Learning
- Week2 - PA 1 - Logistic Regression and Neural Network Mindset
- Week3 - PA 2 - Planar Data Classification With One Hidden Layer
- Week4 - PA 3 - Building Your Deep Neural Network Step By Step
- Week4 - PA4 - Deep Neural Network For Image Classification
Course 2: Improving Deep Neural Networks: Hyperparameter Tuning , Regularization and Optimization
- Week 1 - PA 1 - Initialization
- Week 1 - PA 2 - Regularization
- Week 1 - PA3 - Gradient Checking
- Week 2 - PA4 - Optimization Methods
- Week 3 - PA5 - Tensorflow Tutorial
Course 3: Structuring Machine Learning Projects
- There are no programming assignments in this course.
Course 1: Neural Networks and Deep Learning
- Week 1 Quiz - Introduction To Deep Learning
- Week 2 Quiz - Neural Network Basics
- Week 3 Quiz - Shallow Neural Networks
- Week 4 Quiz - Key Concepts On Deep Neural Networks
Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
- Week 1 Quiz - Practical Aspects of Deep Learning
- Week 2 Quiz - Optimization Algorithms
- Week 3 Quiz - Hyperparameter Tuning ,Batch Normalization , Programming Frameworks
Course 3:Structuring Machine Learning Projects
- Week 1 Quiz - Bird recognition in the city of Peacetopia (case study).md
- Week 2 Quiz - Autonomous Driving(Case Study).md
I am currently doing the fourth course of the specialization. I would add the code base and quiz answers for the same as soon as I am finished with it. Please keep in mind that these assignment and quiz solutions are only for your reference. Use these when you get stuck for a long time. The assignments and quizzes are relatively easy if you can grasp the material well. If you copy/paste the assignments , it would be deemed as cheating, and that is a violation of Coursera's Honor Code. You wouldn't want to do that!
All the best and happy learning. :)