This is a step-by-step tutorial that will guide you through the installation of the necessary libraries and how to setup your environment, in order to run the various code examples.
The code should run on any machine (i.e. Windows, macOS, Linux) that supports Python 3 (64-bit, NOT 32-bit) .
Note: It is possible that the code might work with Python 2, either as is or with a few modifications. However, this repository does not support Python 2. Also, there is no guarantee that individual files will work on their own.
If, for any reason, you want to spend the minimum amount of time for setting up your environment, then you should follow all the steps below.
This installation process requires the minimum knowledge of software tools. It cannot get any easier than this.
In order to save a lot of trouble, download the Anaconda Distribution, which is actively supported by the Data Science community.
- Go to Download page and choose the installation for either Windows, macOS or Linux accordingly.
- Choose Python 3 (64-bit, NOT 32-bit) (latest version is fine, since you can create a new environment after you donwload it)
The Anaconda Distribution also includes the Spyder IDE, which is the one that was used for writting the code examples in this repository. This IDE is considered to be ideal for Machine Learning, Data Science, Scientific Computing, Computational Science and Engineering. An other free option is Pycharm Community Edition.
Note: Anaconda uses conda to create virtual environments, so is cannot be used with venv or virtualenv.
The code examples require several Python packages to be installed. These packages are listed in the requirements.txt file of this folder. You do not need to edit the file for the quick installation.
Currently, there are known issues in TensorFlow with Python 3.7+, so follow the instructions below carefully.
You first have to change the directory to the root of the repository, by replacing <your directory path> accordingly:
cd <your directory path>/artificial_neural_networks
Then, to install the required Python packages and dependencies you have to run the following commands in a terminal:
conda env create -f environment.yml
conda activate ann
Note: For Windows, you should run all of the commands, including the above, at the Anaconda Prompt instead of the Terminal.
You need to download the necessary data for each corresponding code example, as they are not inluded in this repository. To do this, follow the instructions in the datasets folder.
cd <your directory path>/artificial_neural_networks/datasets
If you want to use Docker containers, then you should follow all the steps below. Docker containers are especially useful for Data Science and Research code.
This installation process employs best practises and follows industry standards. So, it is highly recommended.
Docker is a very powerful tool. You can read online about What Docker is and Why to use Docker.
For Windows or Mac:
- Go to Docker Desktop page and choose the installation for either Windows or macOS accordingly.
- Follow the instructions in the page above.
For Linux:
- Go to Docker Community Edition (CE) page and choose the installation for your Linux distribution (e.g. Ubuntu).
- Follow the instructions in the page above.
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If you face any errors, then please try asking at all of the available forums and websites online first. For example:
- StackExchange and any of its sub-forums, such as Data Science
- Reddit and any of its sub-forums, such as Python
- any other website similar to the above
If you have asked at all of the available forums and websites online, then in this case, you can copy-paste the URL links of your questions in this repository and ask your question here. We will try our best to resolve the issue(s).