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Course Content
Module 1: Deep Learning Architectures
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Deep Learning Architectures
06:25
LO1: Define different deep learning architectures such as feedforward networks and CNNs.
11:37
LO2: Explain the structural differences between various neural network architectures.
09:27
LO3: Analyze how architecture selection affects model performance.
10:01
Multiple-Choice Questions
20:00
Module 2: Deep Learning for NLP
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Deep Learning for NLP
00:00
LO1: Identify deep learning models used in natural language processing.
09:15
LO2: Explain how neural networks process textual data in NLP tasks.
08:28
LO3: Apply deep learning models to perform NLP tasks such as classification or sentiment analysis.
08:53
Multiple choice Questions
20:00
Module 3: Recurrent Neural Networks (RNNs)
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Recurrent Neural Networks (RNNs)
00:00
LO1: Explain the structure and working of RNNs and LSTMs.
11:44
LO2: Apply RNN models to sequential data problems.
10:13
LO3: Analyze the advantages and limitations of RNN architectures.
07:48
Multiple-Choice Questions
20:00
Live Sessions
0/3
Live Session: Contract of Guarantee
06:56
Live Session: AWS Services
09:04
Live Session: Full Stack
06:02
Modern World of Artificial Intelligence
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