New features to track and compare multiple deep learning experiments, including visualizing results with custom visualizations.
, released by MathWorks on March 10, 2021, stands as a milestone version that transformed data analysis, algorithm development, and Model-Based Design workflows. This update introduced three completely new products, twelve major toolbox updates, and native code performance improvements that streamlined complex workflows across engineering and scientific disciplines. By optimizing low-level hardware utilization and rewriting core scripting interfaces, the release established a highly interactive environment for developing standalone analytics, processing massive matrix evaluations, and deploying critical production code. Core Environment and Code Development Enhancements
Added more interactive controls (like sliders and drop-down menus) that can be linked to variables in your script for real-time visualization. matlab 2021a
Users can insert dedicated Live Editor tasks to design, configure, and output complex plots interactively. This visual interface automates code generation for clean graphics rendering without manual typing. 2. Streamlined Language Syntax
| Toolbox | Key R2021a Feature | |---------|--------------------| | | New Signal Labeler app for extracting regions of interest from multiple signals. | | ROS Toolbox | Connect to ROS 2 (Foxy Fitzroy) and exchange messages with Python nodes. | | Simulink | Fast Restart now supports referencing of models, reducing simulation iteration time. | | Financial Toolbox | New backtesting engine for Value-at-Risk (VaR) and expected shortfall. | | Image Processing Toolbox | imfuse function now supports color fusion for multispectral imaging. | New features to track and compare multiple deep
% Traditional syntax plot(x, y, 'LineWidth', 2, 'Color', 'r') % R2021a streamlined syntax plot(x, y, LineWidth=2, Color='r') Use code with caution.
A new point-and-click task identifies local maxima and minima in data. Users can adjust threshold parameters interactively, and MATLAB automatically generates the corresponding code. This visual interface automates code generation for clean
Calling Python from MATLAB is now faster. MathWorks optimized the data conversion between MATLAB’s arrays and Python’s numpy.ndarray objects.
A new interactive app lets users design, train, and simulate reinforcement learning policies without writing every line of training loop code.
