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We're here to help you find itDeep Learning with MATLAB Course Overview
Deep Learning with MATLAB certification provides validation for the expertise in designing deep learning models with MATLAB. It demonstrates proficiency in applying deep learning concepts such as Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. This certification is highly valued in various industries. It is useful for industry professionals to create algorithms, develop AI systems, process data, and build prediction models. MATLAB, a leading software in numerical computing, offers an abundance of in-built resources for deep learning. It allows for a simplified yet intricate Computational Modeling. Deep learning with MATLAB is used in industries such as health, finance and technology.
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USD
View Fees Breakdown
Course Fee | 1,075 |
Total Fees |
1,075 (USD) |
USD
View Fees Breakdown
Course Fee | 850 |
Total Fees |
850 (USD) |
USD
View Fees Breakdown
Flexi Video | 16,449 |
Official E-coursebook | |
Exam Voucher (optional) | |
Hands-On-Labs2 | 4,159 |
+ GST 18% | 4,259 |
Total Fees (without exam & Labs) |
22,359 (INR) |
Total Fees (with exam & Labs) |
28,359 (INR) |
♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
The prerequisites for Deep Learning with MATLAB Training typically include:
1. Familiarity with MATLAB: You should have a basic understanding of MATLAB programming, such as working with variables, matrices, and functions.
2. Background in calculus: A foundational understanding of calculus concepts like derivatives, integrals, and partial derivatives is essential for understanding deep learning concepts.
3. Linear algebra knowledge: Concepts such as vectors, matrices, and tensor operations are essential to work with deep learning algorithms.
4. Basic probability and statistics: Practical experience in probability theory and statistics is helpful for understanding concepts like data distribution, estimation, and inference in deep learning.
5. Fundamentals of computer programming: Basic programming skills in a high-level language like Python, C++, or Java is valuable, as it helps you understand programming logic and syntax better.
6. Basic understanding of machine learning: Although not required, it's helpful to have a general idea of machine learning concepts like supervised and unsupervised learning, regression, and classification.
7. Basic knowledge of neural networks: It's beneficial to have a high-level understanding of neural networks, activation functions, and backpropagation before diving into deep learning algorithms in MATLAB.
8. Some experience with image processing or computer vision (optional): This is useful if you plan to focus on deep learning techniques for image recognition and analysis.
To prepare for a Deep Learning with MATLAB course, consider taking introductory courses in programming, linear algebra, calculus, probability, and statistics. MATLAB also offers several resources on its website to help you learn the basics.
Deep Learning with MATLAB certification training equips participants with the knowledge and skills to design, implement, and deploy deep learning applications using MATLAB. The course covers essential topics such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep reinforcement learning. Participants will learn to preprocess data, create neural networks, train models, and deploy solutions to various domains. Additionally, the course provides hands-on experience with real-world projects, enabling learners to solve complex problems in areas like image recognition, natural language processing, and autonomous systems.
Deep Learning with MATLAB course offers a comprehensive understanding of advanced techniques for analyzing complex data. This course benefits you by equipping you with essential skills to build, train, and implement deep neural networks, accelerating your career in data analysis and predictive modeling. Furthermore, it enhances your ability to solve real-world problems in stats, maximizing accuracy and efficiency.