Mxnet is a flexible and efficient library for deep learning. MXNet is developed by collaborators from multiple universities and companies.
Deep Learning Software
• Multiple Languages
• Distributed on Cloud
Small (<50 employees), Medium (50 to 1000 employees), Enterprise (>1001 employees)
Mxnet is a flexible and efficient library for deep learning. MXNet is developed by collaborators from multiple universities and companies. MXNet provides a rich Python API to serve a broad community of Python developers. MXNet offer powerful tools to help developers exploit the full capabilities of GPUs and cloud computing. While these tools are generally useful and applicable to any mathematical computation, MXNet places a special emphasis on speeding up the development and deployment of large-scale deep neural networks. With MXNet, it’s easy to specify where each data structures should live. MXNet makes it easy to scale computation with number of available GPUs. MXNet automates the derivative calculations that once bogged down neural network research. MXNet provides optimized numerical computation for GPUs and distributed ecosystems, from the comfort of high-level environments like Python and R. In addition, MXNet automates common workflows, so standard neural networks can be expressed concisely in just a few lines of code. MXNet supports two styles of programming: imperative programming (supported by the NDArray API) and symbolic programming (supported by the Symbol API). In short, imperative programming is the style that users are likely to be most familiar with. Here if A and B are variables denoting matrices, then C = A + B is a piece of code that when executed sums the values referenced by A and B and stores their sum C in a new variable. Symbolic programming, on the other hand, allows functions to be defined abstractly through computation graphs. In the symbolic style, users first express complex functions in terms of placeholder values. Then, users can execute these functions by binding them to real values.