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Deep Learning and Neural Networks
artificial intelligence and machine learning. It also focuses on the basics of Keras and
TensorFlow, both of which are used in the programming of Deep Learning applications.
The module touches upon the topic of neural networks – both artificial and human. It helps
students to understand how the artificial network functions and the ways it trains itself to
mimic human-like thought process. It focuses on activation functions and various other
applications of deep learning. After the completion of this module, students will be able to
1.Recall the basics of AI-related concepts.
2.Understand supervised and unsupervised machine learning.
3.Conduct a mapping of the various learning‘s that are classified under supervised and
unsupervised machine learning.
4.Comprehend the concept of working of the human brain and its similarities with the artificial
neural network.
5.Develop a working understanding of the neural network applications.
6.Understand the similarities between deep learning and artificial intelligence.
7.Appreciate the similarities and differences between machine learning and deep learning.
8.Understand the basic programming structure required for languages suitable for deep learning.
9.Appreciate the differences and the uses of convolutional and recurrent neural networks.
10.Comprehend the working of sentiments analysis via natural language processing.
TensorFlow, both of which are used in the programming of Deep Learning applications.
The module touches upon the topic of neural networks – both artificial and human. It helps
students to understand how the artificial network functions and the ways it trains itself to
mimic human-like thought process. It focuses on activation functions and various other
applications of deep learning. After the completion of this module, students will be able to
1.Recall the basics of AI-related concepts.
2.Understand supervised and unsupervised machine learning.
3.Conduct a mapping of the various learning‘s that are classified under supervised and
unsupervised machine learning.
4.Comprehend the concept of working of the human brain and its similarities with the artificial
neural network.
5.Develop a working understanding of the neural network applications.
6.Understand the similarities between deep learning and artificial intelligence.
7.Appreciate the similarities and differences between machine learning and deep learning.
8.Understand the basic programming structure required for languages suitable for deep learning.
9.Appreciate the differences and the uses of convolutional and recurrent neural networks.
10.Comprehend the working of sentiments analysis via natural language processing.
37 pages, Kindle Edition
Published June 15, 2022
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