which libraries are the best for developing deep learning scripts in python?
Python has several libraries that are commonly used for developing deep learning scripts. Some of the most popular libraries include:

1. TensorFlow: TensorFlow is an open-source machine learning library that was developed by Google. It provides a wide range of tools and resources for building and training deep neural networks.
2. PyTorch: PyTorch is an open-source machine learning library that is maintained by Facebook. It provides dynamic computational graphs and an easy-to-use interface for building and training deep neural networks.
3. Keras: Keras is a high-level neural networks API that is written in Python. It runs on top of TensorFlow, Theano, and Microsoft Cognitive Toolkit, providing a simple interface for building and training deep learning models.
4. MXNet: MXNet is a flexible and efficient deep learning library that is supported by Amazon Web Services (AWS). It is designed to work well with large datasets and can be used for both research and production.
5. Caffe: Caffe is a deep learning framework that was developed by the Berkeley Vision and Learning Center (BVLC). It is optimized for speed and can be used for both research and production.

Each library has its own unique strengths and weaknesses, and the choice of library will depend on the specific needs of your project.
As a beginner on the topic I do not know which of these I should use. Could you tell me which of these libraries are the most beginner friendly and why?
As a beginner, I would recommend starting with Keras or PyTorch, as they are generally considered more beginner-friendly compared to the other libraries. Here's why:

Keras:
- Keras is designed to be a high-level, user-friendly API for deep learning, which makes it a great choice for beginners.
- It offers a simple and intuitive syntax, allowing you to build and train deep learning models with just a few lines of code.
- Keras provides built-in support for many common deep learning tasks, such as image classification, text processing, and reinforcement learning.
- Since it runs on top of TensorFlow, you can easily switch to using TensorFlow directly if you need more advanced features or customization in the future.

PyTorch:
- PyTorch is known for its dynamic computational graph and eager execution mode, which makes it easier to debug and experiment with deep learning models.
- The library offers a Pythonic interface, making it more familiar and comfortable for those with a background in Python programming.
- PyTorch has extensive documentation and an active community, providing a wealth of tutorials, examples, and support for newcomers.
- It is widely used in research, which means that many state-of-the-art models and techniques are often available first in PyTorch.

Both Keras and PyTorch are excellent choices for beginners, and you can't go wrong with either one. I suggest trying both and seeing which one resonates more with your learning style and project needs. As you gain experience and confidence, you can explore the other libraries to find the one that best suits your specific requirements.
Since I am a complete beginner with little programming experience in the ML / AI field Keras sounds like the safer choice, what do you think? Can you point me to some comparative real world scenarios and show me how the different tools would be implemented?