Demystifying Neural Network: A Beginner's Guide to Machine Learning Fundamentals

Key Takeaways

a neural network is a network of neurons or in a modern sense an artificial neural network composed of artificial neurons it mimics the brains neurons using nodes connections and weights to find an output relative to a specific input a neural network is based on a collection of connected units or nodes called artificial neurons which loosely model the neurons in a biological brain each connection like the synapses in a biological brain can transmit a signal to other neurons an artificial neuron that receives a signal then processes it can send another signal to the neurons connected to it such systems learn to perform tasks by considering examples generally without being programmed with task specific rules for example in image recognition they might learn to identify images that contain cats by analyzing example images that have been manually labeled as cat or no cat and using the results to identify cats in other images they do this without any prior knowledge of cats for example that they have fur tails whiskers and cat-like faces instead they automatically generate identifying characteristics from the examples that they process they only use the pixels value in their neighbors as characteristics computers have no idea what they really are looking at let's explain this with an example if we take an image of a cat and feed it to the neural network the neural network will use all the pixels of an image independently as inputs of the network then the first layer of neurons will work like our retina and find simple features like edges depending on the network the next layers called hidden layers will detect more complex features the inputs will be propagated through the entire network which will all together contribute in finding a final output which is our final decision based on the whole network for this example the output will be cat of course for doing so the weights of the neural network deciding which neurons to use all not to find the right output have to be taught this is done by using back propagation which I explained in a previous video linked in the description [Music]