Intro to AI and Deep Learning

AI Apps:

Video notes:

Video1: Google AI Adventures-What is Machine Learning(ML)?

ML important because of the explosion of data- we need automated systems that can make sense and respond to changes in the data
ML is everywhere-
AI is now an expected feature in new products- personalized, insightful, and self-correcting
Developing AI models has becoming easier with tools such as TensorFlow
AI is using data(training) to answer questions(predictions)
Training- we use our data to fine-tune predictions of a model, that can answer questions about previously unseen data.
Associated article "What is Machine Learning?" → https://goo.gl/Dbxo6M

Video 2: 7 Steps of Machine Learning

Create a model to determine if a drink is wine or beer.

2 features: Color, alcohol%

Step 1: Gather data samples- quality and amount of data will determine accuracy of model

Step 2: Data Preparation-

· put gathered data into a table,

· randomize the order,

· clean up data,

· equal amounts of samples, normalize( all data on same scale)

· split data into training data set and test set (80/20)

Step 3: Choosing a model-

There are various models depending on the application

We have a 2-feature system- small layer model

Step 4: Training the model

Model: Y= m*X +b

where x is data, m is slope, and b is y intercept(bias)- training the model consists of finding optimal values for m and b

w= weights matrix (slope)

b= bias matrix

we start with random values for m and b, then adjust them depending upon the resulting errors

Step 5: Evaluation

Model is tested on new data(test set)

Step 6: Adjust Hyper parameters, Learning Rate( amount parameters are adjusted on each step

Step 7: Make predictions using new data