Andrew NG Notes Collection

This is the first course of the deep learning specialization at Coursera which is moderated by DeepLearning.ai. The course is taught by Andrew Ng.

Andrew NG Machine Learning Notebooks : Reading

Deep learning Specialization Notes in One pdf : Reading

Sr No	Article Reading
1.	Neural Network Deep Learning
2.	Improving Deep learning Network
3.	Structure of ML Projects
4.	Convolutions Neural Network
5.	Sequence Models

Sr. No	MOOC LECTURE LINK
1.	Machine learning by Andrew-NG
	DEEP LEARNING SERIES
1.	Neural Network and Deep Learning
2.	Improving deep neural networks: hyperparameter tuning, regularization and optimization
3.	Structuring Machine Learning Projects
4.	Convolution Neural Network
5.	Sequence Models
6.	CS230: Deep Learning | Autumn 2018

1.Neural Network Deep Learning

• This Notes Give you brief introduction about :
  • What is neural network? How it's work?
  • Supervised Learning using Neural Network
  • Shallow Neural Network Design
  • Deep Neural Network
• Notebooks :
• Week1 - Introduction to deep learning
• Week2 - Neural Networks Basics
• Week3 - Shallow neural networks
• Week4 - Deep Neural Networks

2 Improving Deep learning Network

• This Notes Give you introduction about :
  • Practical aspects of Deep Learning
  • Optimization algorithms
  • Hyperparameter tuning, Batch Normalization and Programming Frameworks
• Notebooks:
  • Week1 - Practical aspects of Deep Learning
    • Setting up your Machine Learning Application
    • Regularizing your neural network
    • Setting up your optimization problem
  • Week2 - Optimization algorithms
  • Week3 - Hyperparameter tuning, Batch Normalization and Programming Frameworks

3.Structure ML Projects

• In This Notes, you can learn about How to Structure Machine Learning Project:
  • Why ML Structure?
  • Error Analysis
• Notebooks:
  • Week1 - Introduction to ML Strategy
    • Setting up your goal
    • Comparing to human-level performance
  • Week2 - ML Strategy (2)
    • Error Analysis
    • Mismatched training and dev/test set
    • Learning from multiple tasks
    • End-to-end deep learning

4.Convolution Neural Network

• Matrix Multiplication Between Image and Kernel Known as Convolution Operation

• In This Notes, you can learn about Brief architecture CNN:
  • Foundations of CNNs
  • Deep convolutional models: case studies
  • Object detection
  • Special applications: Face recognition & Neural style transfer
• Notebooks :
• Week1 - Foundations of Convolutional Neural Networks
• Week2 - Deep convolutional models: case studies
  • Papers for read:
    • ImageNet Classification with Deep Convolutional Neural Networks
    • Very Deep Convolutional Networks For Large-Scale Image Recognition
• Week3 - Object detection
  • Papers for read:
    • You Only Look Once: Unified, Real-Time Object Detection
    • YOLO
• Week4 - Special applications: Face recognition & Neural style transfer
  • Papers for read:
    • DeepFace (Notebook)
    • FaceNet
    • Neural Style Transfer

5.Sequence Models

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• Vanila RNN

  

• LSTM

• GRU

• In This Section, you can learn about Sequence to Sequence Learning

  • Recurrent Neural Networks
  • Natural Language Processing & Word Embeddings
  • Sequence models & Attention mechanism

• Notebooks:

  • Week1 - Recurrent Neural Networks
  • Week2 - Natural Language Processing & Word Embeddings
  • Week3 - Sequence models & Attention mechanism

Thanks for Reading....Happy Learning...!!!