/NeuralNetwork

The 1st course of Deep Learning Specialization on Coursera, offered by deeplearning.ai, instructed by Andrew Ng. Coded by Andrew Chen.

Primary LanguageJupyter Notebook

NeuralNetwork

The 1st course of Deep Learning Specialization on Coursera, offered by deeplearning.ai, instructed by Andrew Ng. Coded by Andrew Chen. Deep learning is the electrcity in a new era. With the improvements of computation ability and the advancement of algorithms, people may have chances to get a deeper insight of the world.

Major Content of this course:

Week 1

  1. Neural Network
  2. Supervised Learning and Unsupervised Learning

Week 2

Logistic Regression

  1. Binary Classification
  2. Logistic Regression
  3. Logistic Regression Cost Function
  4. Gradient Descent
  5. Derivatives
  6. Computation Graph

Python and Vectorization

  1. Vectorization
  2. Vectorizing Logistic Regression
  3. Vectorizing Gradient Descent
  4. Broadcasting in Python

Week 3

Shallow Neural Network

  1. Neural Network Overview
  2. Neural NNetwork Representation
  3. Computing a Neural Network's Output
  4. Vectorzing Across Multiple Examples
  5. Vectorized Implementation
  6. Activation Function
  7. Derivatives of Activation Function
  8. Gradient Descent of Neural Networks
  9. Backpropogation

Week 4

Deep Neural Networks

  1. Deep L-layer Neural Networks
  2. Forward Propogation in Neural Networks
  3. Building Blocks of Neural Networks
  4. Forward and Backward Propogation
  5. Parameters Vs Hyper-parameter
Note

The coder omitted some sections in this repo. No other reasons, just lazy. Happy coding .!