So in this lecture, we will summarize everything we learned in this section, as you saw, the section
was mostly about two things.
Number one, introducing you to TensorFlow syntax.
So now you know all the main steps that go into writing a TensorFlow model, from defining a model to
training it to making predictions.
You learn that training with neural networks isn't as simple as calling model Dutch fit as it is with,
so I can learn things to care as the process is greatly simplified, but it's still less automatic than
most Saikia learn models.
And number two, you saw that the linear models we learned about before are actually models of the neuron,
which are inspired by biology as in real living creatures.
This idea of the neuron is important because this was the inspiration behind deep learning.
Unlike models such as the SVM and decision, trees which are purely mathematical neural networks are
inspired by how biological brains work and how we think as humans.
This is one aspect of deep learning that differentiates it from other forms of artificial intelligence.
You also saw that, unlike psyche, learn understanding the learning process a bit more in depth is
required when using deep learning.
This is really important because as the models we learn about become more complex.
Choosing the right hyper parameters tends to become more difficult.
With linear models, it's easy to choose a learning rate, but with CNN's CNN's and Aunt ends, there
are many more parameters to consider.