summary of neural-networks-deep-learning

Note

This is my personal summary after studying the course neural-networks-deep-learning, which belongs to Deep Learning Specialization. and the copyright belongs to deeplearning.ai.

My personal notes

$1_{st}$ week: introduction-to-deep-learning

• 01_introduction-to-deep-learning
  • [01_What is neural network?](/2018-04-02/01_introduction-to-deep-learning/#01_What is neural network?)
    • [Example 1 – single neural network](/2018-04-02//01_introduction-to-deep-learning/#Example 1 – single neural network)
    • [Example 2 – Multiple neural network](/2018-04-02//01_introduction-to-deep-learning/#Example 2 – Multiple neural network)
• 02_supervised-learning-with-neural-networks
  • [Supervised learning for Neural Network](/2018-04-02/01_introduction-to-deep-learning/#Supervised learning for Neural Network)
  • [Structured vs unstructured data](/2018-04-02/01_introduction-to-deep-learning/#Structured vs unstructured data)
• 03_why-is-deep-learning-taking-off
  • [Why is deep learning taking off?](/2018-04-02/01_introduction-to-deep-learning/#Why is deep learning taking off?)
• 04_about-this-course
  • [Outline of this Course](/2018-04-02/01_introduction-to-deep-learning/#Outline of this Course)

$2_{nd}$ week: neural-networks-basics

• 01_logistic-regression-as-a-neural-network
  • 01_binary-classification
    • [Binary Classification](/2018/02/02/02_neural-networks-basics/#Binary Classification)
    • notation
  • [02_Logistic Regression](/2018/02/02/02_neural-networks-basics/#02_Logistic Regression)
    • [Example: Cat vs No - cat](/2018/02/02/02_neural-networks-basics/#Example: Cat vs No - cat)
    • notation
  • 03_logistic-regression-cost-function
  • 04_gradient-descent
  • 05_06_derivatives
  • 07_computation-graph
  • 09_logistic-regression-gradient-descent
  • 10_gradient-descent-on-m-examples
    • [one single step gradient descent](/2018/02/02/02_neural-networks-basics/#one single step gradient descent)
• 02_python-and-vectorization
  • 01_vectorization
  • 02_more-vectorization-examples
  • 03_vectorizing-logistic-regression
  • 04_vectorizing-logistic-regressions-gradient-output
  • 05_broadcasting-in-python
  • 06_a-note-on-python-numpy-vectors
    • [one rank array](/2018/02/02/02_neural-networks-basics/#one rank array)
    • [practical tips](/2018/02/02/02_neural-networks-basics/#practical tips)
  • 07_quick-tour-of-jupyter-ipython-notebooks
  • 08_explanation-of-logistic-regression-cost-function-optional

$3_{rd}$ week: shallow-neural-networks

• 01_neural-networks-overview
• 02_neural-network-representation
• 03_computing-a-neural-networks-output
• 04_vectorizing-across-multiple-examples
• 05_explanation-for-vectorized-implementation
• 06_activation-functions
• 07_why-do-you-need-non-linear-activation-functions
• 08_derivatives-of-activation-functions
• 09_gradient-descent-for-neural-networks
• 10_backpropagation-intuition-optional
• 11_random-initialization

$4_{th}$ week: deep-neural-networks

• 01_deep-neural-network
• 02_forward-propagation-in-a-deep-network
• 03_getting-your-matrix-dimensions-right
  • [one training example](/2018/02/04/04_deep-neural-networks/#one training example)
  • [m training examples](/2018/02/04/04_deep-neural-networks/#m training examples)
• 04_why-deep-representations
• 05_building-blocks-of-deep-neural-networks
• 06_forward-and-backward-propagation
• 07_parameters-vs-hyperparameters
• 08_what-does-this-have-to-do-with-the-brain

My personal programming assignments

week 1 and week 2: logistic-regression-with-a-neural-network-mindset
week 3: Planar data classification with a hidden layer
week 4 part 1: Building your deep neural network: Step by Step
week 4 part 2: deep-neural-network-application