There are lots of software available for AI. However, the number of software you can use for AI depends on what programming language you choose. So first, you should decide what programming language you are going to use for AI, and then you can get the necessary software and tools available under that programming language. In this guide, I will show you as a beginner how to choose a proper programming language and other necessary components needed to start the AI journey.
Programming Languages For AI
Nowadays, there are a lot of programming languages available for AI. Some of the popular programming languages include Python, R, Julia, and C++. The choice of programming language depends on specific AI tasks, existing libraries and frameworks, community support, performance requirements, and personal preference.
Best Programming Language For AI
Python, because it is very user-friendly and easy to use. Python is a popular programming language for various applications, including web development, data analysis, machine learning, and more. Its simplicity helps us to put more time into the development of AI models rather than complex coding
If you are new to Python, you can install Python as follows:
Windows:
1. Visit the official Python website and Download the Python installer.
2. Run the installer and customize the installation if desired. (recommended to check the box “Add Python to PATH” so that Python can be easily accessed from the command prompt)
3. Install Python by clicking “Install Now”
4. Verify the installation by typing “python–version”
macOS:
1. Open the terminal.
2. Install Python using Homebrew with the command “brew install python”
3. Verify the installation by typing “python3 –version”
Linux:
1. Open the terminal.
2. Update the package manager using “sudo apt update”
3. Install Python 3 with the command “sudo apt install python3”
4. Verify the installation by typing “python3 –version”
Deep Learning Frameworks
Deep learning frameworks are powerful software libraries that are used to simplify the development and training of complex neural networks. With pre-built functions, optimization algorithms, and neural network architectures, these frameworks streamline AI designing and save our time and effort. They offer features like GPU acceleration and automatic differentiation for improving the efficiency of AI training. Deep learning frameworks help us to focus on designing innovative models and solving real-world problems without getting caught up in low-level implementation details like writing code for mathematical operations, defining the architecture of the neural network, handling memory management, and managing computational resources.
Some of the popular deep learning frameworks are PyTorch, TensorFlow, Mxnet, and Keras. The choice between TensorFlow, PyTorch, and MXNet depends on specific requirements, familiarity with the framework, and the community and ecosystem surrounding the framework.
Best Deep Learning framework
PyTorch, PyTorch is a more beginner-friendly and easier-to-understand deep learning framework. Its intuitive Pythonic interface makes it easier to prototype and debug models. PyTorch provides a flexible and intuitive API for building and training neural networks. Also, it offers extensive support for tensor operations, automatic differentiation, and GPU acceleration.
You can install PyTorch as follows:
Windows:
1. Open the Command Prompt.
2. Enter the following command “pip install torch torchvision”
3. Wait for the installation process to complete, Pip will automatically download and install the latest version of PyTorch and its dependencies.
Linux and macOS:
1. Open the terminal.
2. Enter the following command “pip install torch torchvision”
3. Wait for the installation process to complete, Pip will automatically download and install the latest version of PyTorch and its dependencies.
Code Editors for AI
Code editors provide a clean and organized interface for writing and editing our codes more efficiently. Also, code editors provide features like syntax highlighting to improve readability, auto-completion to speed up coding, and error detection to catch mistakes in real-time.
VS Code, Pycharm, Atom, Spyder, and Sublime Text 3 are some popular IDE/code editors.
Best Code Editor
VS Code, Visual Studio Code is a lightweight and powerful code editor developed by Microsoft. It supports various programming languages and provides numerous extensions for enhanced development workflows. One key advantage of VS Code is its IDE capabilities, providing code editing, syntax highlighting, and formatting features. This enables you to write and manage your Python code more efficiently. Additionally, VS Code offers intelligent code suggestions, aiding you in writing accurate and error-free code.
You can install the VS code as follows:
Windows:
1. Visit the official Visual Studio Code website and Download the Visual Studio Code installer for Windows.
2. Run the installer and customize the installation if needed.
3. Install Visual Studio Code.
macOS:
1. Visit the official Visual Studio Code website and Download the Visual Studio Code installer for macOS.
2. Locate the downloaded DMG file and open it.
3. Drag the Visual Studio Code application to the Applications folder.
4. Launch Visual Studio Code from the Applications folder.
Linux:
1. Open the terminal.
2. Download the Visual Studio Code package using “curl -o vscode.deb -L https://go.microsoft.com/fwlink/?LinkID=760868”
3. Install the Visual Studio Code package using “sudo dpkg -i vscode.deb”
4. Install any missing dependencies if prompted.
After all, the choice of programming language, Code editor, and Deep Learning framework depends on the specific requirements of the project and personal preference. When you are more involved in AI projects you will have to use other options too. But for beginners, I highly recommend going with Python and PyTorch. Whichever combination you choose, focus on understanding the fundamental concepts of AI and machine learning, and gradually delve into more complex topics.