Deep Learning Specialization on Coursera

Introduction

I took this coursera course to help me break into the AI field. There are five courses in this specialization.

• Neural Networks and Deep Learning
• Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
• Structuring Machine Learning Projects
• Convolutional Neural Networks
• Sequence Models

This repository includes the course lecture notes, quiz solutions and Jupyter notebooks for each programming exercise.

Build Environment for Neural Networks and Deep Learning

• I created a Test Environment on my local PC to verify I could get the same results as the Jupityer Notebooks (Python 3)
  • Running Python 3.7.4 64-bit | QT 5.9.6 | PyQt5 5.9.2 | Windows 10
  • Used Anaconda /Spyder Python 3.7 IDE
  • Numpy version 1.16.5
  • Tensorflow 2.1.0
  • With this same build environment, one could successfully run Logistic Regrssion Example with test cases

Lecture Notes

• Course1: Neural Networks and Deep Learning
  • Week 1: Introduction to deep learning
  • Week 2: Neural Networks Basics
  • Week 3:Shallow neural networks
  • Week 4: Deep Neural Networks - Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
  • Week 5: Practical aspects of Deep Learning
  • Week 6: Optimization algorithms
  • Week 7: Hyperparameter tuning, Batch Normalization and Programming Frameworks - Course 3: Structuring Machine Learning Projects
  • Week 8: ML Strategy (1)
  • Week 9: ML Strategy (2) - Course 4: Convolutional Neural Networks
  • Week 10: Foundations of Convolutional Neural Networks
  • Week 11: Deep convolutional models: case studies
  • Week 12: Object detection
  • Week 13: Special applications: Face recognition & Neural style transfer

Quiz

• Course1: Neural Networks and Deep Learning
  • Week 1: Introduction to deep learning
  • Week 2: Neural Networks Basics
  • Week 3:Shallow neural networks
  • Week 4: Deep Neural Networks - Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
  • Week 5: Practical aspects of Deep Learning
  • Week 6: Optimization algorithms
  • Week 7: Hyperparameter tuning, Batch Normalization and Programming Frameworks - Course 3: Structuring Machine Learning Projects
  • Week 8: ML Strategy (1)
  • Week 9: ML Strategy (2) - Course 4: Convolutional Neural Networks
  • Week 10: Foundations of Convolutional Neural Networks
  • Week 11: Deep convolutional models: case studies
  • Week 12: Object detection
  • Week 13: Special applications: Face recognition & Neural style transfer

Programming Assignments

• Course1: Neural Networks and Deep Learning
  • Week 2: Neural Networks Basics: Logistic Regression
  • Week 2: Neural Networks Basics: Python Basics
  • Week 3:Shallow neural networks: Planar Data Classification
  • Week 4: Deep Neural Networks: Building Deep Neural Network
  • Week 4: Deep Neural Networks: Deep Neural Network: Application - Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
  • Week 5: Practical aspects of Deep Learning: Gradient Check
  • Week 5: Practical aspects of Deep Learning: Init
  • Week 5: Practical aspects of Deep Learning: Reg
  • Week 6: Optimization algorithms: Optimization
  • Week 7: Hyperparameter tuning, Batch Normalization and Programming Frameworks: TF - Course 3: Structuring Machine Learning Projects - Course 4: Convolutional Neural Networks
  • Week 10: Foundations of Convolutional Neural Networks - Conv Neural Network
  • Week 11: Deep convolutional models: case studies - Res Networks
  • Week 12: Object detection- Autonomous Vehicles
  • Week 13: Special applications: Face recognition & Neural style transfer- Art Generation
  • Week 13: Special applications: Face recognition & Neural style transfer- Face Recognition

License

MIT

Free Software, Hell Yeah!

<> [git-repo-url]: https://github.com/nmazzilli3/deeplearning.ai