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Multilayer Perceptron
4.7221 ratings at Great Learning AcademyOverview
This free Multilayer Perceptron(MLP) course familiarizes you with the artificial neural network, a vastly used technique across the industry. The course starts by introducing you to neural networks, and you will learn their importance and understand their mechanism. You will then go through various applications of neural networks and understand the activation functions, their types, and mechanisms. You will also learn back-propagation and stochastic gradient descent with examples. Lastly, you will go through the demo on neural networks, where you will go through its implementation in various scenarios. Enroll in this free course on Multilayer Perceptron(MLP) and earn a certificate of course completion on completing the quiz at the end of the course.
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What you'll learn
- Multilayer Perceptron (MLP)
Syllabus 7
- Introduction to neural networks
You will understand what Neural Networks are and how they are a pathway to Artificial Intelligence using Deep Learning. This section will also give a brief idea of weights & mathematics in Neural Networks.
- Why do we use Neural Networks?
This module will help you comprehend the importance of neural networks and why we use them in various scenarios.
- Applications of Neural Networks
In this module, you will learn how neural networks play their role in various applications like image processing, speech processing, text processing, gaming, prediction, decision making, Google car, and eyewear.
- How do Neural Networks work?
This module starts by introducing you to fully connected neural networks. Further, you will focus on understanding neural networks' working and the functions of the different layers involved.
- Activation Functions in Neural Networks
This module helps you better understand the working of neural networks by introducing you to the critical function called Activation Functions. You will learn the activation function, its types, and how they function.
- Back-Propagation and Stochastic Gradient Descent
This module familiarizes you with back-propagation and stochastic gradient descent with the help of examples. You will learn the process from scratch and understand the various functions involved.
- Demo on Neural Networks
This module contains hands-on sessions on various scenarios of neural networks on jupyter notebook. This will help you enhance your practical skills and implementation of neural networks.
Advantages and disadvantages
Advantages
- Free, short, with a free certificate.
- Made in India, with examples Indian students will recognise.
- Rated 4.7 out of 5 by 221 learners.
- Free certificate when you finish.
- Completely free.
- Self-paced: start any time.
- A clear syllabus (7 parts) you can see before you start.
- Hands-on: you build or practise, not just watch.
Disadvantages
- Introductory; the certificate carries little weight with employers.
- Expect follow-up calls and emails about paid programs.
Some points apply to every course of this kind; see how we rank.
Free, with a free certificate
- Free: Sign up with your email or phone to watch.
- Certificate: Free when you finish the videos and quiz.
Great Learning is in Tier 4: commercial training companies and platform-made courses of our institution ranking (58/100).
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