Before we dive deeper into neural networks and machine learning, let's make sure that you have set up your computer properly, so that you can run the code in this book smoothly.
In this book, we will use the Python programming language for each neural network project. Along with Python itself, we also require several Python libraries, such as Keras, pandas, NumPy, and many more. There are several ways to install Python and the required libraries, but the easiest way by far is to use Anaconda.
Anaconda is a free and open source distribution of Python and its libraries. Anaconda provides a handy package manager that allows us to easily install Python and all other libraries that we require. To install Anaconda, simply head to the website at https://www.anaconda.com/distribution/ and download the Anaconda installer (select the Python 3.x installer).
Besides Anaconda, we also require Git. Git is essential for machine learning and software engineering in general. Git allows us to easily download code from GitHub, which is probably the most widely used software hosting service. To install Git, head to the Git website at https://git-scm.com/book/en/v2/Getting-Started-Installing-Git. You can simply download and run the appropriate installer for your OS.
Once Anaconda and Git are installed, we are ready to download the code for this book. The code that you see in this book can be found in our accompanying GitHub repository.
To download the code, simply run the following command from a command line (use Terminal if you're using macOS/Linux, and if you're using Windows, use the Anaconda Command Prompt):
$ git clone https://github.com/PacktPublishing/Neural-Network-Projects-with-Python
The git clone command will download all the Python code in this book to your computer.
Once that's done, run the following command to move into the folder that you just downloaded:
$ cd Neural-Network-Projects-with-Python
Within the folder, you will find a file titled environment.yml. With this file, we can install Python and all the required libraries into a virtual environment. You can think of a virtual environment as an isolated, sandboxed environment where we can install a fresh copy of Python and all the required libraries. The environment.yml file contains instructions for Anaconda to install a specific version of each library into a virtual environment. This ensures that the Python code will be executed in a standardized environment that we have designed.
To install the required dependencies using Anaconda and the environment.yml file, simply execute the following command from a command line:
$ conda env create -f environment.ymlJust like that, Anaconda will install all required packages into a neural-network-projects-python virtual environment. To enter this virtual environment, we execute this next command:
$ conda activate neural-network-projects-python
That's it! We are now in a virtual environment with all dependencies installed. To execute a Python file in this virtual environment, we can run something like this:
$ python Chapter01\keras_chapter1.py
To leave the virtual environment, we can run the following command:
$ conda deactivate
Just note that you should be within the virtual environment (by running conda activate neural-network-projects-python first) whenever you run any Python code provided by us.
Now that we've set up our computer, let's return back to neural networks. We'll look at the theory behind neural networks, and how to program one from scratch in Python.
