manojpgupta / coursera-ng-neural-networks-and-deep-learning

Build logistic regression, neural network models for classification

Neural Networks and Deep Learning

Course can be found in Coursera

Quiz and answers are collected for quick search in my blog SSQ

• Week 1:
  • Understand the major trends driving the rise of deep learning.
  • Be able to explain how deep learning is applied to supervised learning.
  • Understand what are the major categories of models (such as CNNs and RNNs), and when they should be applied.
  • Be able to recognize the basics of when deep learning will (or will not) work well.
• Week 2:
  • Build a logistic regression model, structured as a shallow neural network
  • Implement the main steps of an ML algorithm, including making predictions, derivative computation, and gradient descent.
  • Implement computationally efficient, highly vectorized, versions of models.
  • Understand how to compute derivatives for logistic regression, using a backpropagation mindset.
  • Become familiar with Python and Numpy
  • Work with iPython Notebooks
  • Be able to implement vectorization across multiple training examples
  • Python Basics with Numpy (optional assignment)
  • Logistic Regression with a Neural Network mindset
• Week 3:
  • Understand hidden units and hidden layers
  • Be able to apply a variety of activation functions in a neural network.
  • Build your first forward and backward propagation with a hidden layer
  • Apply random initialization to your neural network
  • Become fluent with Deep Learning notations and Neural Network Representations
  • Build and train a neural network with one hidden layer.
  • Build a 2-class classification complete neural network with a hidden layer
• Week 4:
  • See deep neural networks as successive blocks put one after each other
  • Build and train a deep L-layer Neural Network
  • Analyze matrix and vector dimensions to check neural network implementations.
  • Understand how to use a cache to pass information from forward propagation to back propagation.
  • Understand the role of hyperparameters in deep learning
  • Building Deep Neural Network: Step by Step
  • Deep Neural Network for Image Classification: Application