Training and inference phases of neural networks can be analysed individually.
For back propagation or training:
O(n^5) where n is the number of neurons, number of layers and number of iterations.
For forward propagation or inference:
| # For a string | |
| # 'youremywholeworld' | |
| a = input() | |
| # For single integer input | |
| # 9 | |
| a = int(input()) | |
| # For multiple (small) input | |
| # 8 9 |
| # cook your dish here | |
| graph = { | |
| 0: [1, 2], | |
| 1: [2], | |
| 2: [0], | |
| 3: [2] | |
| } | |
| # no of nodes | |
| n = 4 |
| #include <vector> | |
| #include <set> | |
| #include <unordered_set> | |
| #include <cassert> | |
| #include <iostream> | |
| #include <ctime> | |
| #include <unistd.h> | |
| #include <chrono> | |
| #include <algorithm> | |
| #include <vector> |
| """ | |
| a) Images generated by the microscope | |
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