Hey there, you want to become a prodigy in Deep learning or looking out for some cool projects to work on? Then you are in the right place. I welcome you the series of Deep Learning posts.

**What you get out of this series?**

- In-Depth knowledge in Deep Learning
- Easy to fiddle code
- Visualization of things happening in the back-end.

So let's get started with our setup.

**What do you need to get things started?**

- Python-3
- Pip3
- Numpy for python-3
- Matplotlib for python-3
- Jupyter-Notebook

We can use Windows, MAC or Ubuntu. I strongly suggest the use of Ubuntu (It has a lot of community support).

## Installation in Ubuntu

### Installing python3 and pip

- You will already have python3 installed in Ubuntu.
- Press Ctrl+Alt+t to open a terminal
- Install pip3 by typing this command in terminal.
`$ sudo python3 -m pip install -U pip`

- To verify install, type

this would print version of python`$ python3 --version`

- Now you are good to proceed.

### Installing numpy, matplotlib

- In a terminal, type
`$ sudo -H pip3 install numpy matplotlib`

- Now you are done with the installation of numpy and matplotlib

### Installing jupyter notebook

- Open a terminal
- Type
`$ sudo apt update $ sudo apt-get -y install ipython ipython-notebook $ sudo -H pip3 install jupyter`

- To check if it works, type

in a terminal`$ jupyter notebook`

- The above command will open a session of your home directory in your default browser.
- Press,
`Ctrl+c`

, type`y`

and hit`enter`

in the terminal to stop the jupyter notebook.

## Get your hands dirty

- Lets fire up the jupyter notebook by typing the following in the terminal
`$ jupyter notebook`

- This would open a session in your default browser. If not, open a browser and type
`localhost:8888`

in the URL bar.

- That opens a new tab in the browser. This is where we code. (yes we will code in a browser)
- Rename your notebook by changing the untitled to Exercise-1 in the top left corner

Let us get started by importing the required modules. (Type the following lines in the green box and execute the code by pressing shift+enter)

```
import numpy as np
import matplotlib.pyplot as plt
print ("Numpy and matplotlib has been successfully installed.")
```

If everything has been installed without error, then you should see

Numpy and matplotlib has been successfully installed

So what? It works. But *What is Numpy and matplotlib*?

Numpy is a fundamental package for scientific computing. It fast and efficient implementation of matrix multiplication. Matplotlib is also a package used for visualisation. (Feel free to google on these packages to know more)

**Let us create a list of integers.**

```
list_of_integer = [I for I in range(10)] # creating a list of integers
print (type(list_of_integer)) # prints the type of a variable
print (list_of_integer)
```

This code will create a list of integers from 0 to 9 and store it in **list_of_integer**. But these list of integers is not efficient to perform mathematical operations. So we shall convert them to numpy array.

```
x_axis = np.array(list_of_integer)
print (type(x_axis))
print (x_axis)
```

This will create the list_of_integer into a numpy array and store it in x_axis. we shall create another numpy array with random values in the following code.

```
y_axis = np.random.randn(10)
print (y_axis.shape)
```

Let us plot the x_axis and y_axis in the following code.

```
plt.plot(x_axis,y_axis)
plt.ylabel('Random number')
plt.xlabel('x_axis')
plt.show()
```

So with this, the setup is complete. You can now save and shut down the kernel. For the original notebook visit my github page.

## In my next post, you can expect

- Easy-Math behind Neural networks
- Working of a perceptron
- Implementation of a perceptron
- Visualization of a perceptron