So in this video, we will summarize everything we learned in this section, this section introduced
artificial neural networks.
We learned how to build a basic feed for it and then using tensor flow, but more importantly, how
to build more complex architectures as well.
Specifically, we learned how to build a multi input neural network to handle multivariate time series
in a naive way.
We also learned some of the important theory behind neural networks, such as the neuron analogy and
the power of adding more layers with nonlinear activations.
We then discovered what it means to learn.
Although this sounds somewhat magical, it is in fact just a calculus problem.
More specifically, learning is just optimization.
In this section, we apply to deep learning to multiple data sets, we, of course, started with airline
passengers, which has been our benchmark for this cause.
We also apply to deep learning to stock returns, although, as usual, the results may have been a
hard truth to some.
We then learned about a new data set, which is especially well suited to deep learning.
This was human activity recognition.
This is a multiclass classification problem with the multivariate time series as input.
We saw that the multi tailed A&M is useful for processing multivariate time series and can handle them
in a non-native way.
The naive way would be to simply flatten all the data into a single feature vector, but that wouldn't
take advantage of the structure in the Time series.
Now, despite the promise of deep learning, being able to automatically find features, we learn that
sometimes creating features ourselves can still be useful.
We saw that by using predefined features of our Time series data, we were able to obtain a superior
performance.
We also learned how to combine static features with the Time series model to obtain even better performance.