Basically, each connection gets an arrow. It also supports self-connections and gates.
There are some examples of images:
Play around yourself here.
A closely related question and a minimal implementation written in Python.
That program implements the reinforcement learning technique 'Q-Learning'.
The idea is for the program to take in an observation of the environment (which could be a screenshot if learning a computer game, or sensor data for a robot) and output a decision in the form of a vector of ...
I wrote a relatively simple adaptive parser in Prolog. The parser is essentially a string rewriting system that learns new rewrite rules from its input, such as "A implies that B" means "A implies B", or "neither A nor B" means "not (A or B)", using a simple bottom-up parsing algorithm.
Using the grammar rules that it has learned, the parser is able to ...
You might have come across the Tensorflow Playground which has a wonderful visualization of the network connections and the neuron weights.
To emphasize (and this is not emphasized in this answer), in the case of neural networks, the biases or, more precisely, the connections (or weights) between biases and other neurons are also learnable parameters, so the back-propagation algorithm calculates a gradient of the loss function that contains the partial derivatives with respect to the connections ...
In a simple feed-forward network, each artificial neuron has a separate bias value. This allows for greater flexibility for the output layer function than if each neuron had to use a single whole-layer bias. Although not an absolute requirement, without this arrangement it may become very hard to approximate some functions. Moving from a bias vector to a ...
How do I convert this to a number between 0-1 without having access to the original car data?
You save the normalisation parameters (typically an offset and a multiplier for each column), and consider that part of the model. Typically you do this when you originally scale training data.
When you want to re-use the model, as well as loading the neural ...
You have implemented a simple contextual bandit solver, which is a machine learning algorithm. A few details may be different from a full implementation, but the key elements are:
A choice of actions (click hit or don't click hit)
A reward signal that can be observed after each action (+1 for a hit, 0 for nothing happens, -1 for an attack which misses)