Theano is a Python library for efficiently defining, optimizing, and evaluating mathematical expressions involving multi-dimensional arrays. Since its introduction, it has been widely used in machine learning for CPU and GPU mathematical computations. Theano is actively developed and has been used to build many state-of-the-art machine learning models. The paper provides an overview of Theano, its features, recent improvements, and comparisons with other frameworks like Torch7 and TensorFlow. Theano allows symbolic mathematical expressions to be defined and compiled into optimized code for execution on CPUs or GPUs. It supports automatic differentiation, numerical stability, and efficient memory usage. Theano's API is similar to NumPy, allowing users to switch to Theano with a familiar syntax and generate high-performance code for both CPU and GPU. Theano is open-source and has a large, active community contributing to its development and maintenance. Theano's main features include a graph-based representation of mathematical expressions, a compiler for creating functions to compute these expressions, and a library for executing these functions. It supports various types of variables, including tensors, CUDA arrays, and sparse matrices. Theano also provides tools for symbolic differentiation, optimization, and execution of functions. Recent improvements in Theano include enhanced performance through abstract operations for convolution, integration with cuDNN for efficient neural network operations, and the introduction of a new GPU backend based on libgpuarray. Theano also supports data parallelism with Platoon, allowing the use of multiple GPUs for model parallelism. Additionally, Theano has improved graph optimization, faster compilation, and better visualization and debugging tools. Theano has been compared with other machine learning frameworks like Torch7 and TensorFlow. The benchmarks show that Theano performs well on various models, including convolutional networks, recurrent neural networks, and sequence-to-sequence models. Theano's performance is competitive with other frameworks, and it continues to be actively developed and improved.Theano is a Python library for efficiently defining, optimizing, and evaluating mathematical expressions involving multi-dimensional arrays. Since its introduction, it has been widely used in machine learning for CPU and GPU mathematical computations. Theano is actively developed and has been used to build many state-of-the-art machine learning models. The paper provides an overview of Theano, its features, recent improvements, and comparisons with other frameworks like Torch7 and TensorFlow. Theano allows symbolic mathematical expressions to be defined and compiled into optimized code for execution on CPUs or GPUs. It supports automatic differentiation, numerical stability, and efficient memory usage. Theano's API is similar to NumPy, allowing users to switch to Theano with a familiar syntax and generate high-performance code for both CPU and GPU. Theano is open-source and has a large, active community contributing to its development and maintenance. Theano's main features include a graph-based representation of mathematical expressions, a compiler for creating functions to compute these expressions, and a library for executing these functions. It supports various types of variables, including tensors, CUDA arrays, and sparse matrices. Theano also provides tools for symbolic differentiation, optimization, and execution of functions. Recent improvements in Theano include enhanced performance through abstract operations for convolution, integration with cuDNN for efficient neural network operations, and the introduction of a new GPU backend based on libgpuarray. Theano also supports data parallelism with Platoon, allowing the use of multiple GPUs for model parallelism. Additionally, Theano has improved graph optimization, faster compilation, and better visualization and debugging tools. Theano has been compared with other machine learning frameworks like Torch7 and TensorFlow. The benchmarks show that Theano performs well on various models, including convolutional networks, recurrent neural networks, and sequence-to-sequence models. Theano's performance is competitive with other frameworks, and it continues to be actively developed and improved.