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Theano is a Python library for building and training deep learning models. It was developed by the Montreal Institute for Learning Algorithms (MILA) at the University of Montreal and released in 2009. The library is known for its efficient implementation of mathematical operations on multidimensional arrays, which allows for faster computation of neural networks.

Some of the key features of Theano include:

1. Symbolic computation: Theano allows developers to define mathematical expressions symbolically, rather than numerically. This allows for easier implementation of complex mathematical models, including deep neural networks.

2. GPU acceleration: Theano can be used with NVIDIA GPUs to accelerate the computation of neural networks, making it possible to train models much faster than on CPU-only systems.

3. Automatic differentiation: Theano includes an automatic differentiation engine that can compute gradients of functions defined by a user, which is essential for training neural networks.

4. Integration with NumPy: Theano integrates well with NumPy, a Python library for scientific computing, making it easy to use multidimensional arrays in Theano computations.

5. Code optimization: Theano includes a range of optimizations that can improve the speed and efficiency of neural network computations.

Overall, Theano is a powerful and efficient library for building and training deep learning models. Its support for symbolic computation and automatic differentiation, along with its integration with NumPy and GPU acceleration, make it a popular choice for machine learning researchers and developers. However, development of Theano has been discontinued since 2017, and many researchers have shifted to other libraries such as TensorFlow and PyTorch.