MATLAB and Simulink Seminars

Big Data Analytics and Deep Learning with MATLAB


The combination of smart connected devices with big data analytics and deep learning is enabling a wide range of applications, from advanced driver assistance systems to sophisticated predictive maintenance systems. Developers of modern data analytics and deep learning applications face many challenges regarding handling large data sets, developing robust and optimized models, and working with new computing paradigms, such as cloud and GPU computing. Engineering and IT teams are using MATLAB to address these challenges while building and deploying advanced big data analytics systems.


In this seminar, we will demonstrate how MATLAB can help you

  • Handle big data from streaming sensors, local files, databases & HDFS in the form of numerics, text, images, and videos
  • Compare and refine machine learning models using approaches such as logistic regression, classification trees, support vector machines, and ensemble methods 
  • Build deep learning models using Convolutional Neural Networks, Directed Acyclic Graph networks, and Long Short Term Memory networks
  • Leverage high-performance computing resources, such as multicore computers, GPUs, computer clusters to scale up the performance of big data and deep learning models 
  • Deploying developed models with enterprise applications hosted on cloud platforms or to embedded devices

Who Should Attend

  • Data scientists, big data architects, deep learning enthusiasts, computer vision engineers

There is no fee for this seminar. Please note this seminar is not designed for students. If you are a student, please do not register for this seminar. You will be required to show a professional ID card to gain access to the seminar.


Time Title

09:30 – 10:00


10:00 – 10:15

Introduction and Welcome Address

10:15 – 11:45 

Developing and Deploying Big Data Analytics with MATLAB
  • Accessing and pre-processing data from Local Files, Databases and Hadoop File Systems
  • Using MATLAB’s library of machine learning algorithms to build predictive analytics
  • Using long and short term memory (LSTM) networks for time series data
  • Scaling up to speed up and handle big data
  • Deploy developed analytics on clusters and cloud platforms

11:45 – 12:15 


12:15 – 13:45 

Developing and Deploying Deep Learning-Based Computer Vision Systems

  • Use deep learning for object detection, image recognition, and semantic segmentation
  • Import popular models like AlexNet, VGG, GoogLeNet, and perform transfer learning
  • Accelerate the process of labelling ground-truth data
  • Deploy computer vision and deep learning systems in hardware using automatic MATLAB to CUDA code generation

13:45 – 14:00

Wrap up and Q&A 



Product Focus

Registration closed