Introduction to Neural Networks

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    Neural Networks

    Explore neural networks to build solutions

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    Artificial Neural Networks (ANN)

    Neural networks for classification and regression

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    Convolutional Neural Networks (CNN)

    Work on computer vision applications

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    Recurrent Neural Networks (RNN)

    Achieve natural language processing in real-life

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Overview

Overview

Course Overview

Neural Networks are all about interconnecting artificial neurons to solve a business problem. It is imitating the human brain which is a natural neural network. As part of this exploration learn about Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and finally Recurrent Neural Networks (RNN). These form part of the neural network systems used to solve real-life problems.

Project-based learning
It is essential to apply the knowledge to convert it into a skill. All our programs follow project-based learning pedagogy with assignments and quizzes.
Individual Projects
Post completion of the course, student is expected to complete 2 projects with varying difficulty to showcase learning.
Solution Orientation
All the courses would encourage students to understand the real-life problems and attempt to design solutions using AI technologies.

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28 SESSIONS

E-learning videos followed by master sessions

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17 HR ENGAGEMENT

Balancing your knowledge & skills

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MASTER CLASSES

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REAL-TIME SOLUTIONS

AI solutions for real-life problems.

Curriculum

What Does U Get ?

Introducing Deep Learning

Explore the neural network concept to understand the difference between natural and artificial neural networks. Check the functional model of the brain.

Understanding Data

Learn to identify types of data and work with both structural and unstructured data. Work with images, text, and datasets of various sizes.

Libraries for AI

Discover libraries that make Python powerful for achieving neural networks. Work with OpenCV, TensorFlow, Keras, and other libraries to implement deep learning.

Activation Functions

Get introduced to activation functions used to predict outcomes. Cover popular linear and non-linear functions to solve problems.

Artificial Neural Networks

Learn about Artificial Neural Networks (ANN) along with the essentials for building your first neural network. Work on classification and regression problems.

Convolutional Neural Networks

Upgrade to Convolutional Neural Networks (CNN) to start working on computer vision applications for problem solving.

Recurrent Neural Networks

Explore Recurrent Neural Networks (RNN) to understand their architecture and applications in the domain of Natural Language Processing (NLP).

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Pricing

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Online short course with self-guiding topics with on-demand support from experts.

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