- Use MATLAB with over 1,000s hardware devices
- Integrate MATLAB into your production applications.
- Integrate with code and other languages, like C, C++, Java, .NET, .COM, Python etc.
- Run algorithms faster by scaling up to clusters, the cloud, and GPUs with only minimal code changes.
- Plug into Simulink for Model-Based Design.
- Automatically convert MATLAB code to embeddable C, CUDA, and HDL or verilog code.
If you want to understand MATLAB and what kind of applications you can develop in MATLAB
- Machine and Deep Learning
- Automotive
- Aerospace
- Industrial Automation
- Digital Twin
- Signal Processing and Communications
- Physical Modeling
- Image Processing and Computer Vision
- Complex Logic
- Discrete-Event Simulation
- Systems Engineering
- Large-Scale Modeling
- Georeferenced Data Analysis
Data Import and Visualisation in MATLAB
If you have historical data and if you want quick visualisation, mathemtical equation generation and finding missing data, then you can watch below recorded video.
Objectives
- Data fitting
- Parametric fitting
- Prediction
Solution
- Basic fitting in MATLAB
Control System Design with Simulink
If you want to learn, how to design Simulink model and auto tuning parameters of PID controller, then this video will be helpful for you.
Learn Data Preprocessing and Data Analytics in 22 minutes
Capability of live script for data processing, mathematical Modelling and algorithm development.
An application on Industrial IoT
Learn to create a predictive model to identify faulty and non faulty components of a plant, in 30 minutes.
India COVID-19 Patients Analysis with MATLAB and Optimization Techniques
Machine Learning Vs Mathematical Modeling
Mathematical models are kind of static model that represent a natural/real phenomenon in mathematical form; the models once formulated does not necessitate to change the form after they are formed.
Machine Learning or Deep Learning models are enough flexible to change as per arrival of new data as they can incorporate new and emerging patterns and trends; this is where pattern recognition/clustering come into focus.
Objectives
- Data Fitting
- Parametric fitting
- Prediction
Solution
- Fit using curve fitting
Machine Learning and Deep Learning with MATLAB
Learn emotion detection with Deep Learning and diabetics patient detector with Machine Learning.
Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data.
Deep learning is a type of supervised machine learning in which a model learns to perform classification tasks directly from images, text, or sound. Deep learning is usually implemented using a neural network. The term “deep” refers to the number of layers in the network—the more layers, the deeper the network.
Optimization with MATLAB
Optimization Types in MATLAB
Problem-Based Optimization
- Easier to create and debug
- Only for linear or quadratic problems with linear or integer constraints
- Represent the objective and constraints symbolically
- Solution time is longer because of translation time from problem form to matrix form
The problem-based approach currently applies to:
- Linear programming problems
- Mixed-integer linear programming problems
- Quadratic programming problems
- Linear least-squares problems
Solver-Based Optimization
- Represent the objective and constraints as functions or matrices
- Solution time is shorter because there is no translation time to matrix form
Click here to learn Optimization
Model Based Design Using Simulink
Learn MBD and system modeling with Simulink.
The example is based on the movement of solar panel according to the Sun movement, to increase the solar efficiency.
Learn Deep Learning in 14 Minutes Without Writing a Single Code
MATLAB provide a Deep Network Designer app to design deep Learning network layers, uploading the data, training the data and validating the model
Learn to use Deep Network Designer in MATLAB. Also learn how to import pre-trained models in MATLAB.
For more recorded webinars on different applications click here
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