# Add new sudo user to linux

```adduser username usermod -aG sudo username su - username```

` sudo systemctl reload php7.0-fpm.service `

# Some kind of plots

### Distribution (Histogram)

```import matplotlib.pyplot as plt df.hist(bins=50, figsize=(20, 15)) plt.show()```

### Distribution (Density)

A density plot is a smoothed, continuous version of a histogram estimated from the data.

```df.plot(kind='density', subplots=True, layout=(8,8), sharex=False, legend=False, figsize = (12,12)) plt.show()```

### Correlations

```corr = train_df.corr() corr```

### Heatmap (needs correlations)

```%matplotlib inline import seaborn as sns plt.figure(figsize = (16,8)) sns.heatmap(corr, annot = True)```

### Histogram plot of select variable(s)

```from matplotlib import pyplot pyplot.subplot(211) pyplot.hist(train_X.iloc[:, 1]) pyplot.subplot(212) pyplot.hist(train_X.iloc[:, 2])```

# Machine Learning: Pima Indians Diabetes

### Visualise the Dataset

Visualising the data is an important step of the data analysis. With a graphical visualisation of the data we have a better understanding of the various features values distribution: for example we can understand what’s the average age of the people or the average BMI etc…We could of course limit our inspection to the table visualisation, but we could miss important things that may affect our model precision.

```import matplotlib.pyplot as plt dataset.hist(bins=50, figsize=(20, 15)) plt.show()```

# Deep Learning: Which Loss and Activation Functions should I use?

### Summary Table

The following table summarizes the above information to allow you to quickly find the final layer activation function and loss function that is appropriate to your use-case # Get Started: 3 Ways to Load CSV files into Colab – Towards Data Science

```from google.colab import files uploaded = files.upload()```
```import io df2 = pd.read_csv(io.BytesIO(uploaded['Filename.csv']))```
``` ```