Commit d5a0f415 authored by Björn Oberli's avatar Björn Oberli
Browse files

Saved changes to:

	notebooks/Convolutional Neural Networks/Solutions to Exercises - Convolutional Neural Networks.ipynb
	notebooks/Convolutional Neural Networks/lab.ipynb
	notebooks/Preliminaries Numpy and Pandas/Checking Correct Installation.ipynb
parent 3d6d8743
Pipeline #446984 passed with stage
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%% Cell type:markdown id: tags:
### Checking your installation
Please check that the notebook below runs smoothly.
%% Cell type:code id: tags:
``` python
import sys
sys.version #Should work and give 3.7.
```
%%%% Output: execute_result
'3.7.6 | packaged by conda-forge | (default, Mar 23 2020, 23:03:20) \n[GCC 7.3.0]'
'3.9.12 | packaged by conda-forge | (main, Mar 24 2022, 23:25:59) \n[GCC 10.3.0]'
%% Cell type:code id: tags:
``` python
import tensorflow as tf
tf.__version__ #Should work and give 2.7.1
```
%%%% Output: execute_result
%%%% Output: error
'2.7.1'
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
Input In [2], in <cell line: 1>()
----> 1 import tensorflow as tf
2 tf.__version__
ModuleNotFoundError: No module named 'tensorflow'
%% Cell type:code id: tags:
``` python
import numpy as np
np.__version__ #Should work and give something > 1.19
```
%%%% Output: execute_result
'1.19.1'
%% Cell type:code id: tags:
``` python
import matplotlib.pyplot as plt
%matplotlib inline
plt.scatter(range(100), np.sin(0.1 * np.array(range(100))))
```
%%%% Output: execute_result
<matplotlib.collections.PathCollection at 0x7fd13651a050>
%%%% Output: display_data
![](data:image/png;base64,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)
%% Cell type:code id: tags:
``` python
```
......
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