Note
This is my personal note at the first week after studying the course neural-networks-deep-learning and the copyright belongs to deeplearning.ai.
01_introduction-to-deep-learning
01_What is neural network?
It is a powerful learning algorithm inspired by how the brain works.
Example 1 – single neural network
Given data about the size of houses on the real estate market and you want to fit a function that will predict their price. It is a linear regression problem because the price as a function of size is a continuous output.
We know the prices can never be negative so we are creating a function called Rectified Linear Unit (ReLU) which starts at zero.
The input is the size of the house (x)
The output is the price (y)
The “neuron” implements the function ReLU (blue line)
Example 2 – Multiple neural network
The price of a house can be affected by other features such as size, number of bedrooms, zip code andwealth. The role of the neural network is to predicted the price and it will automatically generate the hidden units. We only need to give the inputs x and the output y.
02_supervised-learning-with-neural-networks
Supervised learning for Neural Network
In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output.
Supervised learning problems are categorized into “regression“ and “classification“ problems. In a regression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function. In a classification problem, we are instead trying to predict results in a discrete output. In other words, we are trying to map input variables into discrete categories.
Here are some examples of supervised learning.
There are different types of neural network, for example Convolution Neural Network (CNN) used often for image application and Recurrent Neural Network (RNN) used for one-dimensional sequence data such as translating English to Chinses or a temporal component such as text transcript. As for the autonomous driving, it is a hybrid neural network architecture.
Structured vs unstructured data
Structured data refers to things that has a defined meaning such as price, age whereas unstructured data refers to thing like pixel, raw audio, text.
03_why-is-deep-learning-taking-off
Why is deep learning taking off?
Deep learning is taking off due to a large amount of data available through the digitization of the society, faster computation and innovation in the development of neural network algorithm.
Two things have to be considered to get to the high level of performance:
- Being able to train a big enough neural network
- Huge amount of labeled data
The process of training a neural network is iterative.
It could take a good amount of time to train a neural network, which affects your productivity. Faster computation helps to iterate and improve new algorithm.
04_about-this-course
Outline of this Course
- Week 1: Introduction
- Week 2: Basics of Neural Network programming
- Week 3: One hidden layer Neural Networks
- Week 4: Deep Neural Networks
