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.

<Span style="color:red;">Andrew NG Machine Learning Notebooks : Reading

<Span style="color:red;">Deep learning Specialization Notes in One pdf : Reading

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

• 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...!!!