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