Deep Learning detailed syllabus for Artificial Intelligence & Data Science (AI&DS) for 2021 regulation curriculum has been taken from the Anna University official website and presented for the AI&DS students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.
For Artificial Intelligence & Data Science 5th Sem scheme and its subjects, do visit AI&DS 5th Sem 2021 regulation scheme. The detailed syllabus of deep learning is as follows.
Course Objectives:
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Unit I
DEEP NETWORKS BASICS
Linear Algebra: Scalars — Vectors — Matrices and tensors; Probability Distributions — Gradientbased Optimization – Machine Learning Basics: Capacity — Overfitting and underfitting -Hyperparameters and validation sets — Estimators — Bias and variance — Stochastic gradient descent — Challenges motivating deep learning; Deep Networks: Deep feedforward networks; Regularization — Optimization.
Unit II
CONVOLUTIONAL NEURAL NETWORKS
Convolution Operation — Sparse Interactions — Parameter Sharing — Equivariance — Pooling -Convolution Variants: Strided — Tiled — Transposed and dilated convolutions; CNN Learning: Nonlinearity Functions — Loss Functions — Regularization — Optimizers –Gradient Computation.
Unit III
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Unit IV
MODEL EVALUATION
Performance metrics — Baseline Models — Hyperparameters: Manual Hyperparameter — Automatic Hyperparameter — Grid search — Random search — Debugging strategies.
Unit V
AUTOENCODERS AND GENERATIVE MODELS
Autoencoders: Undercomplete autoencoders — Regularized autoencoders — Stochastic encoders and decoders — Learning with autoencoders; Deep Generative Models: Variational autoencoders -Generative adversarial networks.
Course Outcomes:
After the completion of this course, students will be able to:
- Explain the basics in deep neural networks
- Apply Convolution Neural Network for image processing
- Apply Recurrent Neural Network and its variants for text analysis
- Apply model evaluation for various applications
- Apply autoencoders and generative models for suitable applications
Text Books:
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Reference Books:
- Salman Khan, Hossein Rahmani, Syed Afaq Ali Shah, Mohammed Bennamoun, ”A Guide to Convolutional Neural Networks for Computer Vision”, Synthesis Lectures on Computer Vision, Morgan & Claypool publishers, 2018.
- Yoav Goldberg, ”Neural Network Methods for Natural Language Processing”, Synthesis Lectures on Human Language Technologies, Morgan & Claypool publishers, 2017.
- Francois Chollet, ”Deep Learning with Python”, Manning Publications Co, 2018.
- Charu C. Aggarwal, ”Neural Networks and Deep Learning: A Textbook”, Springer International Publishing, 2018.
- Josh Patterson, Adam Gibson, ”Deep Learning: A Practitioner’s Approach”, O’Reilly Media, 2017.
For detailed syllabus of all other subjects of Artificial Intelligence & Data Science, 2021 regulation curriculum do visit AI&DS 5th Sem subject syllabuses for 2021 regulation.
For all Artificial Intelligence & Data Science results, visit Anna University AI&DS all semester results direct link.