ANNs vs. CNNs
Test 1.
1. If we give an input matrix of size (30x30x64) to the flattened layer, what will be its size?
900
57600 Ans: You Selected
1920
576
Why?:
Problem in non-convex, it's to find local minimum and global minimum, because it's not visible. The learning rate of the gradient can take any way to descend, not in a restrictive way determined.
In the left graph the slope goes from negative and goes increasing(in multidimension convex function, any direction you move, the slope goes always increasing).
In the right graph the slope goes at the begin increasing and then decreasing(in non-convex function).
The lower the local minimum is the better the algorithm is.
2 important Things:
- We don't need to know and explore 100% of the surface in a non-convex function.
- We need to get the most quickly search of the local mininum
1.1 Deep Learning History - 1
We have almost 86 billons of neurons.
Sinapsis.
Take the ouput signals or results, and send it into another neural.
The Neural Netwerks try to emulate that a brain makes by means a computer.
It's like we put a magnifying glass or a microscope ans see how the information goes through the neurons and how it reaches the ouput.
Each neuron is called perceptron.
1. Deep Learning Overview
Test 1.
Use cases:
Sephora: Clustering-Unsupervised Learning, to cluster the clients with their preferences.
GoogleMind
Tesla: Image processing
Google Translator: NLP