MATLAB

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.

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.