MATLAB is a numerical computing and programming platform that enables users to develop and implement mathematical algorithms, create models and analyze data. Designed for engineers and scientists, it can be used for a range of purposes, including deep learning and machine learning, computational finance, image processing, predictive maintenance, IoT analytics and more. Built around its matrix-based programming language, it can help users run analyses on large data sets as well as design and rigorously test models.

The Computer-Aided Engineering (CAE) system was created by software company MathWorks to help users analyze data, develop algorithms, and create models. It boasts of professionally developed tools that passed rigorous testing and complete documentation. Interactive apps display how various algorithms work with users’ data. The technology is also scaleable so that analyses can run on clusters, GPUs, and clouds with only minimal code changes, thus eliminating the need to rewrite or learn big data programming.

MATLAB solutions for Deep Learning allow users to design, build, and visualize convolutional neural networks using just a few lines. Even with just limited technical knowledge, users can run deployed models up to 7 times faster than other similar software. Users can access updated models such as GoogLeNet, VGG-16, VGG-19, AlexNet, ResNet-50, ResNet-101, and Inception-v3. MATLAB is integrated with a variety of solutions for image processing and computer vision. Users can obtain images and videos from imaging hardware, utilize graphical tools to visualize and manipulate them, and migrate them to embedded software. Libraries of reference-standard algorithms also help generate newer, fresher ideas.

MATLAB makes it easy to use signal processing techniques to explore and analyze time-series data. The software delivers a unified workflow for the development of embedded systems and streaming applications. Signals can be obtained, measured, and assessed from multiple sources. Streaming algorithms for audio, smart sensor, instrumentation, and IoT devices can be developed, tested, and implemented using MATLAB’s extensive features.

MATLAB is beneficial for financial organizations as it develops quality-assured, transparent, documented, and replicable risk and stress testing models. It boasts of easy-to-use tools so that models can be created in just a matter of days, which is important given today’s rapidly changing regulatory and business environments. Model and operational risks can be avoided as the software lets users incorporate “risk-aware” developer best practices when implementing model control and automation. A single risk model stack can service multiple compliance regimes and front and middle office functions, thus making organizations more productive.

MATLAB is useful for robotics researchers and engineers. The technology can design and tune algorithms, model real-world systems, and automatically generate code all using one integrated platform. Users can connect to and control their robot with developed algorithms. Tools to generate hardware-agnostic algorithms and connect to the Robot Operating System (ROS) are also available. Users can send control signals or analyze multiple data types by connecting to a range of sensors and actuators.