AresScripts

Definitely an interesting subject to follow and/or keep a close eye on

I dont think we will ever be human. Its good at doing small tasks, sometimes better than humans. Not many tasks. Part of the limit with current AI is hardware. We simply dont have powerful enough processors to do backpropagation on really big neural networks. As time goes on that will change and we will come up with better methods for Machine Learning, but I still believe that it will never get to the point of being as smart as a human. Just my humble opinion though. I am often wrong

I assume that was a recurrent neural network? I plan to get into convolutional neural networks by the end of the series

The area of Neural Networks has originally been primarily inspired by the goal of modeling biological neural systems, but has since diverged and become a matter of engineering and achieving good results in Machine Learning tasks. Nonetheless, we begin our discussion with a very brief and high-level description of the biological system that a large portion of this area has been inspired by. BIOLOGICAL MOTIVATIONS AND CONNECTIONS The basic computational unit of the brain is a neuron. Approximately 86 billion neurons can be found in the human nervous system and they are connected with approximately 10^14 - 10^15 synapses. The diagram below shows a cartoon drawing of a biological neuron (first model) and a common mathematical model (second model). Each neuron receives input signals from its dendrites and produces output signals along its (single) axon. The axon eventually branches out and connects via synapses to dendrites of other neurons. In the computational model of a neuron, the signals that travel along the axons (e.g. x0x0) interact multiplicatively (e.g. w0x0w0x0) with the dendrites of the other neuron based on the synaptic strength at that synapse (e.g. w0w0). The idea is that the synaptic strengths (the weights ww) are learnable and control the strength of influence (and its direction: excitory (positive weight) or inhibitory (negative weight)) of one neuron on another. In the basic model, the dendrites carry the signal to the cell body where they all get summed. If the final sum is above a certain threshold, the neuron can fire, sending a spike along its axon. In the computational model, it is assumed that the precise timings of the spikes do not matter, and that only the frequency of the firing communicates information. Based on this rate code interpretation, the firing rate of the neuron is with an activation function ff, which represents the frequency of the spikes along the axon. Historically, a common choice of activation function is the sigmoid function σσ, since it takes a real-valued input (the signal strength after the sum) and squashes it to range between 0 and 1. Models mentioned above: These neurons, when put together in groups, create networks known as Neural Networks. The architecture of a Neural Network can be broken down into layers. There are input layers, hidden layers, and output layers. Left: A 2-layer Neural Network (one hidden layer of 4 neurons (or units) and one output layer with 2 neurons), and three inputs. Right: A 3-layer neural network with three inputs, two hidden layers of 4 neurons each and one output layer. Notice that in both cases there are connections (synapses) between neurons across layers, but not within a layer. Using more hidden layers will give the Neural Network more accuracy at the expense of being very computationally intensive and thus take much longer to train in some cases. The more complex a problem is, the more hidden layers and hidden neurons will be needed. As you can see, the more hidden neurons shown above, the more precise the network can be in its answer. Data points are shown as dots. Their color represents the expected value, and the shaded regions represent the output of the network at that given position. i.e. a green dot in a red region means the network has incorrectly predicted the data. You can play with this yourself at http://cs.stanford.edu/people/karpathy/convnetjs/demo/classify2d.html Thanks for reading! Let me know if you would like me to continue the tutorials. It may seem dry right now but as you get deeper and deeper into this topic it becomes much more interesting. Heres a little extra video for those who are new to Machine Learning: -Ares

Do you have java installed?

They dont ban immediately. Once they are sure that someone is a bot they watch the account for a few days and collect data on the behaviours.

Theres a script for this

Maybe he doesnt have java

I dont like how your profile picture theme does not match your siggy theme.

Nope! It supports duradel now

I can see you are absorbing American culture well

Remember that its important to know your audience. Your topics should be tied into things that all of the kids(or most) can relate to. For example: you can explain conditionals by asking them "If its really snowy outside where do we have recess." They will probably respond with "inside" to which you can show them how they just made an if statement.... if it is snowy outside -> recess is inside....if it is not snowy outside -> recess is outside.

Best upgrade I see is some Liptons tea

Can we make the site look less like a porn site?

Ahahahahahahahaha wow

Its a pretty hard concept to grasp untill you have a strong foundation. Once you understand it, however, you will be hooked on programming.

GUIs are really hard if you dont know much about Object Oriented Programming(OOP) yet

Try printing out the player name. I doubt it has anything to do with .equals()

Im exactly the same way

I generally need dead silence when i program but sometimes that just isn't possible. Can anyone recommend me an artist or a music genre that is pretty quiet. Ive looked into chillstep but it tends to be too wubby for me. Just looking for some good background music. Thanks! -Ares

You have to apply for each of the Scripter ranks. Scripter 1 is pretty easy to get. Scripter 2 is harder, and Scripter 3 is really hard.

Ray Bands

Excited to start working?