Unit 1
Introduction to Tensorflow, Installing and learning its basics, Recap of Neural networks, Convolution neural networks(CNN) and Recurrent Neural Networks (RNN)
| Course Name | Advanced Topics in Deep Learning |
| Course Code | 24CSC331 |
| Program | 5 Year Integrated MSc/ BSc. (H) in Mathematics with Minor in Data Science |
| Semester | Elective |
| Credits | 3 |
| Campus | Amritapuri |
Introduction to Tensorflow, Installing and learning its basics, Recap of Neural networks, Convolution neural networks(CNN) and Recurrent Neural Networks (RNN)
Autoencoder and Decoders, Introduction to Generative Adversarial networks (GANs)
Introduction to Speech Processing, important neural network architectures used in them
Introduction to Natural Language processing (NLP), Important neural network architectures used in them
Course outcomes
CO1: To understand the fundamentals of deep learning
CO2: To know the main techniques in deep learning and the main research in this field.
CO3: Be able to design and implement deep neural network systems,
CO4: Be able to autonomously extend the knowledge acquired during the study course by reading and understanding scientific and technical documentation.
CO5: Identify new application requirements in the field of computer vision.
Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press.
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