Introduction To Neural Nets

Ben Gorman has a two-part series introducing neural networks.  First, the basics behind neural networks:

We can solve both of the above issues by adding an extra layer to our perceptron model. We’ll construct a number of base models like the one above, but then we’ll feed the output of each base model as input into another perceptron. This model is in fact a vanilla neural network. Let’s see how it might work on some examples.

Then, he digs into the mathematics of backpropagation:

Our problem is one of binary classification. That means our network could have a single output node that predicts the probability that an incoming image represents stairs. However, we’ll choose to interpret the problem as a multi-class classification problem – one where our output layer has two nodes that represent “probability of stairs” and “probability of something else”. This is unnecessary, but it will give us insight into how we could extend task for more classes. In the future, we may want to classify {“stairs pattern”, “floor pattern”, “ceiling pattern”, or “something else”}.

Our measure of success might be something like accuracy rate, but to implement backpropagation (the fitting procedure) we need to choose a convenient, differentiable loss function like cross entropy. We’ll touch on this more, below.

This is definitely a series to read after you’ve gotten your coffee.

Related Posts

Real-Time Data Visualization With R And SQL Server

Tomaz Kastrun shows how simple it can be to plot real(ish)-time data from SQL Server using R: In the previous post, I have showed how to visualize near real-time data using Python and Dash module.  And it is time to see one of the many ways, how to do it in R. This time, I will […]

Read More

Plotting ML Results In R

Bernardo Lares shows off the plots he creates in R to compare ML models: Split and compare quantiles This parameter is the easiest to sell to the C-level guys. “Did you know that with this model, if we chop the worst 20% of leads we would have avoided 60% of the frauds and only lose […]

Read More

Categories

December 2017
MTWTFSS
« Nov Jan »
 123
45678910
11121314151617
18192021222324
25262728293031