Installing libraries

Setting up Python3 for deep learning involves several steps, including installing Python3, setting up a virtual environment, installing deep learning libraries, and configuring your development environment. Here's a step-by-step guide:

Step 1: Install Python 3

Download and install Python 3 from the official Python website https://www.python.org/downloads/ . Follow the installation instructions for your operating system (Windows, macOS, or Linux).

You can install Python 3 using the command line, follow these steps:

Open a command prompt or terminal window on your computer.
Navigate to the directory where you want to install Python 3 (if necessary).
Enter the appropriate command for your operating system:
- For Windows :
```
choco install python
```
- For macOS :
```
brew install python
```
- For Ubuntu/Linux :
```
sudo apt-get update
sudo apt-get install python3
```
Follow the prompts to complete the installation process.
Verify the installation by running python3 command in the command prompt or terminal to start the Python 3 interpreter.
```
python3 --version
```

Setp 2: Install pip

Pip is a package manager for Python that allows you to easily install and manage Python packages. Here's how you can install pip on different operating systems:

For Windows :

Download the get-pip.py script from https://bootstrap.pypa.io/get-pip.py.
Open a command prompt with administrative privileges.
Navigate to the directory where you downloaded the get-pip.py script.
Run the following command to install pip:
```
python get-pip.py
```

For macOS and Linux :

Open a terminal window.
Run the following command to download the pip installer script :
```
curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
```
Run the following command to install pip :
```
python3 get-pip.py
```
Once the installation is complete, you can use pip to install Python packages by running pip install <package_name> in the command prompt or terminal. Pip will automatically handle dependencies and install the requested packages.

Note

Note : If you're using Python 3, you should use pip3 instead of pip in the commands above to ensure that you're installing packages for Python 3 specifically.

Step 3: Set up a Virtual Environment

It is recommended to set up a virtual environment to isolate your deep learning project dependencies from your system-wide Python installation. You can use virtualenv or conda to create a virtual environment.
Need to install libary virtualenv . Run the following command
```
  pip install virtualenv
```
To create a virtual environment using virtualenv, open a command prompt or terminal and run the following command
```
  python3 -m venv myenv
```
where myenv is the name of your virtual environment. You can replace python3 with python if you are using Windows.

Step 4: Activate the Virtual Environment

Activate the virtual environment using the appropriate command for your operating system:
- For Windows:
```
myenv\Scripts\activate
```
- For macOS and Linux:
```
source myenv/bin/activate
```
- You can deactivate using
```
deactivate
```

Step 4: Install Deep Learning Libraries

Install the required deep learning libraries using pip, the Python package manager, while your virtual environment is active. Some popular deep learning libraries include TensorFlow, Keras, PyTorch, and scikit-learn.
```
pip install tensorflow keras pytorch scikit-learn
```

Step 5: Configure Your Development Environment

Depending on your deep learning workflow, you may also need to configure other tools such as an Integrated Development Environment (IDE), code editor, and other libraries or dependencies specific to your project.

Step 1: Install Python 3​

Setp 2: Install pip​

Step 3: Set up a Virtual Environment​

Step 4: Activate the Virtual Environment​

Step 4: Install Deep Learning Libraries​

Step 5: Configure Your Development Environment​