Deep learning with Python refers to the application of deep learning techniques using the Python programming language. Python is a popular choice for deep learning due to its simplicity, flexibility, and the availability of powerful libraries and frameworks specifically designed for deep learning tasks.

Here are some key libraries and frameworks commonly used for deep learning in Python:

1. TensorFlow: Developed by Google, TensorFlow is one of the most widely used deep learning frameworks. It provides a comprehensive ecosystem of tools, libraries, and community resources for building and deploying deep learning models.

2. Keras: Keras is a high-level neural network API that runs on top of TensorFlow, allowing for easy and fast prototyping of deep learning models. It provides a user-friendly interface for building various types of neural networks.

3. PyTorch: PyTorch is an open-source deep learning framework developed by Facebook’s AI Research lab. It is known for its dynamic computation graph and provides a more intuitive and flexible approach to building deep learning models.

4. Theano: Although it is less commonly used now, Theano is a popular library for deep learning in Python. It allows for efficient computation on both CPUs and GPUs and provides a symbolic expression framework for defining and optimizing mathematical expressions.

5. Caffe: Caffe is a deep learning framework primarily used for computer vision tasks. It provides a high-level interface and supports various deep learning architectures, making it easy to experiment with pre-trained models.

6. scikit-learn: scikit-learn is a general-purpose machine learning library that also includes functionality for deep learning. While it may not offer the same level of customization and performance as specialized deep learning libraries, it can be useful for simpler deep learning tasks or as an introduction to deep learning concepts.

These libraries offer a wide range of functionality for building and training deep neural networks, including support for convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative models, and more. They also provide tools for data preprocessing, visualization, and evaluation of deep learning models.

To get started with deep learning in Python, you can explore tutorials, online courses, and documentation provided by the respective libraries. It’s also beneficial to have a good understanding of Python programming, linear algebra, and calculus, as these concepts are fundamental to deep learning.