coursera-ng-neural-networks-and-deep-learning's Introduction

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

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