Hello. In this lesson, you will learn how to train and evaluate deep learning models. Deep learning is
an advanced form of machine learning that tries to emulate the way
the human brain learns. In your brain, you
have nerve cells called neurons which
are connected to one another by nerve
extensions that pass electrical chemical signals
through the network. When the first neuron in
the network is stimulated, the input signal is processed. If it exceeds a
particular threshold, the neuron is
activated and passes a signal on to the neurons
in which it is connected. These neurons in turn
may be activated and pass the signal on through
the rest of the network. Over time, the connections between the neurons
are strengthened by frequent use as you learn
how to respond effectively. For example, if someone
throws a ball towards you, your neuron connections
enable you to process the visual information and coordinate your movements
to catch the ball. If you perform this
action repeatedly, the network of neurons involved in catching
a ball will grow stronger as you learn how to be better at catching a ball. Deep learning emulate this
biological process using artificial neural
networks that process numeric inputs rather than
electrochemical stimuli. The incoming nerve
connections are replaced by numeric inputs that are
typically identified as X. When there's more
than one input value, X is considered a vector
with elements named X1, X2, and so on. Associated with each X
value is a weight W, which is used to
strengthen or weaken the effect of the X value
to simulate learning. Additionally, a bias, b input is added to enable fine-grain control
over the network. During the training process that W and b values will be adjusted to tune the network so that it learns to produce
correct outputs. The neuron itself encapsulates a function that calculates
a weighted sum of X, W, and b. This function is in
turn enclosed in an activation function that
constrains the result, often to a value
between zero and one to determine whether or not the neuron passes
an output on to the next layer of
neurons in the network.