Deep learning is the secret recipe behind many exciting developments and has made many of
our world 's dreams and nightmares come true. the course includes everything you need to get
started with deep learning in Python how to build remarkable algorithms capable of solving
complex problems that weren't possible just a few decades ago. we'll talk about the difference
between artificial intelligence and machine learning.. deep learning is a machine learning
technique that learns features and tasks directly from data by running inputs through a
biologically inspired neural network architecture.. The technique is a subfield of machine
learning where algorithms are inspired by the structure of the human brain. Just like neurons
make up neural networks. information propagates through three central components that form
the basis of every neural network architecture. the input layer is the input layer, the output layer
and several hidden layers between the two. each. Neuron in turn is associated to a numerical
value called the bias, which is then added to the input sum.. This weighted sum is then passed
through a nonlinear function called the activation function, which essentially decides if that
particular neuron can contribute to the next layer. the neuron with the highest value determines
what the output is..
a new neural network works you feed input. The network initializes with random weights and
biases that are adjusted each time. the more data you give the newer network the better. It will
be at predicting the right output, but there 's a tradeoff because too much data and you 'll end up
with a problem like overfitting which I 'll discuss. Later in this course. this learning algorithm can
be summarized as follows As follows First. We take a set of input data and pass them through
the network. we compare these predictions obtained with the values of the expected labels and
calculate the loss using a loss function.. we then perform back propagation in order to
propagate this loss to each and every weight and bias. We use this propagated information to
update the weights and biases of neural network with the gradient descent algorithm. a sigmoid
function. is a nonlinear activation function where the activation is proportional to the input by a
value called the slope of the line.. this way, it gives us a range of activations, so it is n't binary
activation. We can connect a few neurons together and if more than one fires, we could take the
maximum value and decide based on that so that is okay too..
our world 's dreams and nightmares come true. the course includes everything you need to get
started with deep learning in Python how to build remarkable algorithms capable of solving
complex problems that weren't possible just a few decades ago. we'll talk about the difference
between artificial intelligence and machine learning.. deep learning is a machine learning
technique that learns features and tasks directly from data by running inputs through a
biologically inspired neural network architecture.. The technique is a subfield of machine
learning where algorithms are inspired by the structure of the human brain. Just like neurons
make up neural networks. information propagates through three central components that form
the basis of every neural network architecture. the input layer is the input layer, the output layer
and several hidden layers between the two. each. Neuron in turn is associated to a numerical
value called the bias, which is then added to the input sum.. This weighted sum is then passed
through a nonlinear function called the activation function, which essentially decides if that
particular neuron can contribute to the next layer. the neuron with the highest value determines
what the output is..
a new neural network works you feed input. The network initializes with random weights and
biases that are adjusted each time. the more data you give the newer network the better. It will
be at predicting the right output, but there 's a tradeoff because too much data and you 'll end up
with a problem like overfitting which I 'll discuss. Later in this course. this learning algorithm can
be summarized as follows As follows First. We take a set of input data and pass them through
the network. we compare these predictions obtained with the values of the expected labels and
calculate the loss using a loss function.. we then perform back propagation in order to
propagate this loss to each and every weight and bias. We use this propagated information to
update the weights and biases of neural network with the gradient descent algorithm. a sigmoid
function. is a nonlinear activation function where the activation is proportional to the input by a
value called the slope of the line.. this way, it gives us a range of activations, so it is n't binary
activation. We can connect a few neurons together and if more than one fires, we could take the
maximum value and decide based on that so that is okay too..
